Improving farmers’ crop yield is crucial to the agriculture sector’s sustainable growth and economic advancement in India. AI improves agriculture by optimising planting, harvesting, and resource utilisation through the analysis of soil, weather, and crop health data. It helps with crop selection, pest control, and disease identification, boosting yields while reducing waste. By supporting sustainable farming, this data-driven strategy aids farmers in adjusting to shifting market and environmental conditions. In order to maximise crop recommendation accuracy, this study consists of dataset visualization and comparison several Machine Learning models using optimised hyperparameter tuning. This effort attempts to determine the best model for recommending ideal crops by evaluating model performance under various circumstances, ultimately assisting farmers in making data-driven decisions. In addition to increasing yields, this strategy strengthens the agricultural framework, which supports the objectives of sustainable growth and economic development in the Indian agricultural sector.