Because rainfall forecasting has a direct impact on crop yield, resource management, and overall farm productivity, it is essential to agricultural planning. Rainfall patterns are influenced by a number of internal and external factors, such as variations in temperature, humidity, atmospheric pressure, and monsoon variability. Rainfall forecasting accuracy is crucial for maximizing irrigation schedules, preserving soil moisture, and guaranteeing sustainable farming methods. Rainfall patterns that are unpredictable result in crop failures, inefficient use of resources, and financial losses for farmers. Using past weather data and climate parameters, this review combines cutting-edge machine learning and deep learning models to increase the accuracy of rainfall predictions. These models enhance forecasting accuracy by analyzing historical weather patterns and environmental variables, thereby increasing readiness for severe weather events. For farmers, policymakers, and agricultural researchers to develop data-driven strategies for mitigating climate-related risks, accurate rainfall forecasts provide vital insights. Using Karnataka’s main crops as a case study, this study expands on forecasted rainfall data by estimating crop yield. Accurate yield forecasts are crucial for both food security and economic stability, as climate variations have a significant impact on agricultural productivity. The effectiveness of DL models in predicting agricultural output is analyzed through a review of published literature and forecasting analyses presented between 2020 and 2025, focusing on trends in methodologies, results, and research gaps rather than providing new empirical rainfall or agricultural yield estimates. This review helps improve food security, optimize resource use, and make informed decisions by leveraging advancements in climate modeling and agricultural analytics. The results provide a thorough framework for flexible farming practices, guaranteeing sustainable crop yields in the face of changing environmental.