Over the past two decades, agricultural research and innovation have increased with the aim of maximising earnings and minimising expenses. Climate change and deteriorating soil quality provide a substantial obstacle in this regard and have a considerable impact on the ability to anticipate with any degree of accuracy, which crops will thrive in which places. An intelligent and creative agricultural system has been put in place to address these present issues in the agricultural industry. In the suggested method, Machine Learning (ML) technique is used to develop a predictive model for identifying better and healthier crops based on current meteorological conditions. Random Forest (RF) classifiers is an example of the ML algorithms that have been used. In order to train these algorithms, soil-related data was used to accurately predict which crops are best suited for cultivation under specific condition. The overall accuracy achieved was an impressive 98% for RF. Among the classifiers, the RF classifier performed the best with an accuracy rate of 98%. Additionally, various performance metrics were analyzed and evaluated for this study. This research has important implications for farmers as it enables them to predict the most suitable crops for cultivation based on prevailing weather conditions. By incorporating innovative technology and automation into the agricultural system, the risk of crop failure can be reduced, ultimately leading to increased income for farmers.