Modeling of rainfall is the most challenging aspect of the hydrological processes because of the intricacy of atmospheric dynamics. In a country like India, where agriculture is the main source of income, long-term rainfall forecasting is critical. For modeling and analyzing rainfall data, the location of Surat (Gujarat) was chosen, and using an artificial neural network (ANN) with various meteorological indices, distinct properties of the hidden neurons in the system were studied. For this analysis, 39 years of data (from 1981 to 2019) were used. The data was utilized for training in 75% of the cases, validation in 15% of the cases, and testing in 15% of the cases. The models were discovered to be capable of accurately predicting rainfall. In this study, it was observed that using neural network tool to implement the algorithm achieved the best results when compared to neural fitting tool, i.e., a multilayer algorithm outperforms a single layer algorithm in terms of performance. The mean square error, which was found to be 0.3620, was used to measure the model’s overall performance. Also, water resource planning and management can benefit from this artificial neural network modeling. Modeling rainfall is a highly challenging aspect of hydrological processes due to the complexity of atmospheric dynamics. In India, where agriculture is the main source of income, long-term rainfall forecasting is critical. This study focuses on modeling and analyzing rainfall data in Surat, Gujarat, using an artificial neural network (ANN) with various meteorological indices to examine the distinct properties of the hidden neurons in the system. We utilized 39 years of data from 1981 to 2019, partitioning it into 75% for training, 15% for validation, and 15% for testing. The ANN models demonstrated a high capability for accurately predicting rainfall. Notably, the study found that using the neural network tool to implement the algorithm achieved superior results compared to the neural fitting tool, indicating that a multilayer algorithm outperforms a single-layer algorithm in terms of performance. The model’s overall performance was assessed using the mean square error, which was found to be 0.3620. The findings underscore the effectiveness of ANN in capturing the intricate patterns in rainfall data, leading to reliable predictions. This modeling approach offers significant potential for enhancing water resource planning and management, providing a valuable tool for agricultural stakeholders in regions dependent on rainfall. The study contributes to the broader understanding of hydrological modeling by highlighting the strengths of ANN in handling complex meteorological data sets and offering practical solutions for forecasting. In conclusion, the study confirms the feasibility and accuracy of ANN in long-term rainfall forecasting, particularly in regions where agriculture is heavily dependent on rainfall. By leveraging the capabilities of multilayer algorithms, this research provides a robust framework for future studies and practical applications in water resource management. The observed mean square error indicates a high level of precision in the model’s predictions, making it a reliable tool for policymakers and agricultural planners. This comprehensive analysis of rainfall using ANN sets a precedent for further research into advanced modeling techniques for other hydrological variables and regions, ultimately aiming to improve predictive accuracy and resource management in agriculture-dependent economies.