India’s economy and employment depend heavily on agriculture, which is the country’s backbone. Poor crop selection is one of the biggest causes of loss in the agricultural industry. The majority of farmers are also ignorant about the soil’s needs in terms of nutrients, minerals, moisture content, and other factors. To improve this situation, a model that suggests the best crop based on factors like soil and weather can be developed. If implemented properly, modern technologies like machine learning and deep learning have the potential to revolutionize these industries. We provide a special technique for crop recommendation based on soil and meteorological information. Agriculture, especially, depends significantly on soil and environmental elements for predicting crop outcomes. Historically, farmers held authority over choosing crops, overseeing their growth, and determining the optimal harvest timing. Today, the agricultural community finds it challenging to carry on due to the fast changes in the environment. As a result, deep learning approaches have increasingly replaced traditional prediction methods. This work has employed a number of these methods to calculate agricultural production. Employing effective feature selection techniques is essential to guarantee that a deep learning model operates with a high degree of precision.