Indian agriculture stands as a plinth of the nation’s economy, characterized by its diversity and profound significance. It employs a substantial portion of the population and contributes significantly to the GDP. However, amidst modern technological advancements and evolving global markets, both opportunities and challenges emerge for India’s agricultural sector. This study aimed to comprehensively evaluate machine learning-based systems for predicting agricultural yields. The India district-wise agriculture crop dataset underwent meticulous preprocessing, including standard scaling and one-hot encoding of categorical variables, to prepare it for modeling. Among various algorithms tested, the Bagging Regressor emerged as the most effective. Leveraging grid search optimization, the model, named YieldYaan (YY), achieved an impressive R2 value of 0.97 with minimal errors (6.32). In order to validate the YY model, MAE, RMSE, and R2 were employed to assess its effectiveness across five key crops, including sugarcane, coconut, rice, wheat, and potatoes. These findings not only highlight the potential of machine learning to revolutionize agricultural output forecasting but also offer valuable insights for crop management and resource allocation decisions. Implementing the YY model widely in agricultural decision-making processes holds promise for enhancing the effectiveness and sustainability of farming practices. This approach could optimize crop yields, mitigate risks, and contribute to the resilience of India’s agricultural landscape in a dynamic global environment by leveraging advanced analytics.