<p>In this study, the focus was on the development and evaluation of a Rainfall-Runoff model for the Banjar River Catchment in Madhya Pradesh, India. The investigation integrated the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique, leveraging both neural network and Fuzzy Logic principles. Given the increasing frequency of extreme weather events and the need for accurate runoff predictions, this research is crucial for effective water resource management. The ANFIS model parameters were determined through a meticulous process, utilizing the Hybrid Optimization Method for training. The model's performance was assessed using statistical criteria, particularly the coefficient of determination (R²). Results indicated that ANFIS with Hybrid Optimization exhibited superior predictive capabilities compared to other methods, showcasing its efficiency in runoff prediction. The study also recommended potential extensions, including the application of similar methodologies to other river basins in India, the exploration of alternative rainfall-runoff modeling methods, the consideration of longer time spans for model development, and the exploration of different membership functions for ANFIS. This comprehensive approach contributes valuable insights to hydrological modeling and runoff prediction.</p>