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 SnFe
2O
4/g-C
3N
4 heterojunction nanocomposite, successfully synthesized from graphitic carbon nitride (g-C
3N
4) and tin ferrate (SnFe
2O
4) 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 SnFe
2O
4 and g-C
3N
4 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 SnFe
2O
4 and g-C
3N
4, the SnFe
2O
4/g-C
3N
4 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 SnFe
2O
4 and g-C
3N
4, as well as the efficient separation of photoexcited electron/hole pairs. The recyclability of the SnFe
2O
4/g-C
3N
4 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 SnFe
2O
4/g-C
3N
4 nanomaterial. The main parameters (photocatalyst dose, initial dye concentration, H
2O
2 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 R
2 values of 0.97 for RSM and 0.99 for ANN.
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