Recently, the energy demand has increased, and the utilization of technologies for renewable energy has expanded significantly; thus, previous works have examined the role of these technologies in different perspectives of energy research problems, employing various MADM (multi attribute decision making) approaches. Renewable energy has become an integral component of sustainable economic development; hence several studies have been carried out to determine the investment strategies in renewable energy alternatives. For instance, the AHP method was used for solar energy [
24] investigation and the selection of locations for thermal power plants [
25] in India, solar farm sites determination in Turkey [
26], biofuels and fossil fuels comparison [
27], and wind observation station location selection [
28] in different countries. On the other hand, many novel techniques and MADM approaches, such as ANP (Analytic Network Process), AHP, VIKOR, ELECTRE, and TOPSIS were developed to rank the alternative systems and to optimize the energy systems. The MADM approaches have been employed to reduce uncertainties in energy growth, wherever different investors are involved in the decision-making, considering a broad range of economic, social, technical, and environmental aspects [
29]. The world primary energy need is increasing due to the rapid economic development [
30]. Sustainability in energy resources and prioritizing the renewable energy system are therefore important mechanisms. In this context, Vishnupriyan and Manoharan [
31] presented sustainability in limited energy resources by integrating a renewable energy system with a grid to meet energy demand using AHP and stochastic multi-attribute acceptability analysis. The decision-makers selected the important criteria using fuzzy AHP based type 2 fuzzy sets, and fuzzy multi attribute decision-making approaches for energy resources prioritization. Siksnelyte et al. [
32] reviewed sustainable decision-making to solve energy problems and determine the contradictory effects using MADM approaches. In this perspective, Mardani et al. [
29,
32] carried out detailed analyses of MADM methods and applications in energy systems. They labeled MADM and fuzzy MADM approaches into the following groupings: AHP, VIKOR, TOPSIS, PROMETHEE, fuzzy sets, and system and ANP are mostly used for impact analysis, energy technology evaluation, and for the selection of the best place for energy generation. Krishankumar et al. [
33] discussed the framework of ranking the alternatives utilizing the developed interval-valued probabilistic linguistic term set (IVPLTS)-based classical VIKOR approach. Alizadeh et al. [
34] combined two models—Benefit, Opportunity, Cost, Risk, and ANP models—to determine the solar energy as the preferential renewable energy source for Iran. In decision-making, the DMs should not only consider the costs of energy systems, but also the energy systems’ efficiency and their environment protection abilities [
35]. Therefore, DEA (Data Envelopment Analysis), TOPSIS, and COPRAS (Complex Proportional Assessment of alternatives) were employed to investigate the priority of energy systems investment and sensitivity analysis under different scenarios. Ilbahar et al. [
36] reviewed the utilization and evaluation of renewable energy sources using MADM for several determinations, particularly energy policies and criteria used for geographic distribution and the determination of application areas [
37]. Krishankumar et al. [
38] proposed a new decision framework of MADM to extend the COPRAS method to q-ROFS for the prioritization of objects and aggregate preference matrix by prioritizing the renewable energy source in India. Shmelev and Bergh [
39] verified the selection of the most suitable renewable energy source for electricity generation, optimal site identification [
40], and the selection of the best alternative energy options [
40,
41]. For instance, Yazdani-Chamzini et al. [
42] applied integrated AHP-COPRAS and novel approaches [
43] to select the best alternative renewable energy projects. Acar and Dincer [
44] used five main measures for the selection of hydrogen production methods using hesitant fuzzy AHP. An integrated fuzzy [
45] and comparative analysis of hybrid decision-making with balanced scorecard-based [
46] approaches were used for investment analysis of renewable energy alternatives. Carrico et al. [
47] investigated the optimal energy-efficient options in water systems using Ant Colony and ELECTRE-III to solve the multi-criteria GDM problems. Bhowmik et al. [
48] used TOPSIS to find the optimal green energy source. Rani et al. [
49] proposed a new divergence measure for ranking and choosing the renewable energy sources in MCDM problems based on the fuzzy TOPSIS approach to compare some existing methods. Lee and Chang [
41] employed PROMETHEE to evaluate five different energy sources. Celikbilek and Tuysuz [
50] used fuzzy multi-attribute GDM to make pairwise comparison energy systems to find the best alternatives. Energy source selection is a complex problem, many criteria and sub criteria, such as technical, environmental, social, and economic needs, must be considered. The technological maturity, reliability, safety, the impact on ecosystems, social benefits, and social acceptability are good examples of immeasurable sub-criteria of energy systems [
51]. These criteria and sub-criteria sets are naturally vague and imprecise, and need the domain experts’ judgments for clarification. Therefore, fuzzy set theory-based approaches were integrated with AHP, ANP, VIKOR, TOPSIS, and the other decision-making methods for the evaluation of renewable energy systems and elimination of imprecision. For instance, fuzzy AHP, fuzzy ANP, fuzzy DEMATEL (decision-making trial and evaluation laboratory), fuzzy TOPSIS [
52], and fuzzy ELECTRE approaches were uncovered with the aim of determining the priority of energy systems. Colak and Kaya [
53] employed the integrated fuzzy approaches for criteria prioritization and decision-making. Ren [
54] established a novel multi-attribute GDM method and combined with the interval AHP intuitionistic fuzzy distance-based method to prioritize energy storage technologies. In this context, Siksnelyte et al. [
32] and Ilbahar et al. [
36] carried out extensive works on MADM approaches and fuzzy sets theory and determined that about 27% of total publications are about sustainable energy systems. On the other hand, they found that the distribution of methods by application areas of energy systems are as follows: 16.67% are AHP and ANP applications, 8.33% are fuzzy applications, 5.36% of VIKOR are about energy policy and energy project selection. Additionally, the distribution of application areas by method are as follows: in 15.76% of energy policy papers AHP and ANP were used, in 10.35% fuzzy sets, and in 28.57% of energy systems papers VIKOR was employed. The MADM and fuzzy MADM approaches are labeled into the following methods: AHP, VIKOR, TOPSIS, PROMETHEE, and ANP, that are mostly used for impact analysis. The fuzzy sets and system, AHP, TOPSIS, ANP, and PROMETHEE methods are mainly applied for energy technology evaluation. The AHP and fuzzy sets are applied for the selection of the best place for energy generation. Although the costs of energy systems are important criteria, the energy systems’ efficiency, its environment protection ability, abundancy, and availability are more important criteria for several countries. Therefore, AHP, DEA, TOPSIS, and COPRAS were employed to evaluate and investigate the energy system priority and sensitivity under different decision-making scenarios. The utilization and evaluation of renewable energy sources particularly using MADM for several determinations, such as energy policies, criteria evaluation, geographic distribution, and the application areas, are very common lately. It was also determined that fuzzy AHP has serious application in energy systems research. Hence, Kahraman et al. [
55] performed a comparison analysis using fuzzy AHP and found that wind energy was the best alternative in Turkey. Lee et al. [
56] determined that the hydrogen energy technologies are the best choice for implementation. Sánchez-Lozano et al. [
57] integrated the GIS (provided the database containing the alternatives), and fuzzy AHP and fuzzy TOPSIS methods to identify the optimal places for solar PV (photovoltaic) power plants in southeast Spain. Yunna and Geng [
58] examined the best location for solar thermoelectric power plants. Fuzzy sets-based linguistic interval preferences modeling was also used with these integrated MADM approaches to better handle uncertainty of the decision-making processes in energy systems research. It was also determined that ELECTRE was the second MADM approach, followed by TOPSIS and VIKOR; ANP and DEMATEL (decision-making trial and evaluation laboratory) are the third most preferred methods for site selection and alternative location evaluation [
32,
52,
55,
56,
57,
58]. Similarly, our results and findings show that solar photovoltaic energy is the best choice for the Kingdom.