This research proposes a loan approval mechanism for agriculture using machine learning techniques to predict loan status, approved amounts, and crop recommendations for declined applications. This model uses features such as land area, soil quality index, farming experience, credit score, and predicted rainfall besides historical loan data to improve decision-making accuracy. The model that predicts rainfalls would be integrated in such a way that the financial risks amongst the farmers and banks decrease, thus stabilizing the agricultural economies. Therefore, it addresses the concerns put across areas such as Maharashtra where the farmers are not putting the financial resources in the right time. The predictive analytics models optimize crop planning. It also optimizes loan decision approval such that the loan defaults are reduced. In this respect, this AI-based system helps to remove the bias that exists in traditional loan appraisals by embracing a much more efficient, transparent, and data-driven process. This would enhance the decision-making capacity of financial institutions, leading to farmers choosing the right crop and resources for sustainable agriculture and food security.