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The development of heterostructures incorporating photocatalysts optimized for visible-light activity represents a major breakthrough in the field of environmental remediation research, offering innovative and sustainable solutions for environmental purification. This study explores the photocatalytic capabilities of a SnFe₂O₄/g-C₃N₄ heterojunction nanocomposite, successfully synthesized from graphitic carbon nitride (g-C₃N₄) and tin ferrate (SnFe₂O₄) and applied to the degradation of the cationic dye brilliant cresyl blue (BCB) in an aqueous solution. These two components are particularly attractive due to their low cost and ease of fabrication. Various characterization techniques, including XRD, FTIR, SEM, and TEM, were used to confirm the successful integration of SnFe₂O₄ and g-C₃N₄ phases in the synthesized catalysts. The photocatalytic and photo-Fenton-like activity of the heterojunction composites was evaluated by the degradation of brilliant cresyl blue under visible LED illumination. Compared to the pure components SnFe₂O₄ and g-C₃N₄, the SnFe₂O₄/g-C₃N₄ nanocomposite demonstrated a superior photocatalytic performance. Furthermore, the photo-Fenton-like performance of the composites is much higher than the photocatalytic performances. The significant improvement in photo-Fenton activity is attributed to the synergistic effect between SnFe₂O₄ and g-C₃N₄, as well as the efficient separation of photoexcited electron/hole pairs. The recyclability of the SnFe₂O₄/g-C₃N₄ composite toward BCB photo-Fenton like degradation was also shown. This study aimed to assess the modeling and optimization of photo-Fenton-like removal BCB using the SnFe₂O₄/g-C₃N₄ nanomaterial. The main parameters (photocatalyst dose, initial dye concentration, H₂O₂ volume, and reaction time) affecting this system were modeled by two approaches: a response surface methodology (RSM) based on a Box–Behnken design and artificial neural network (ANN). A comparison was made between the predictive accuracy of RSM for brilliant cresyl blue (BCB) removal and that of the artificial neural network (ANN) approach. Both methodologies provided satisfactory and comparable predictions, achieving R² values of 0.97 for RSM and 0.99 for ANN. Full article