As the Internet of Things (IoT) has advanced, life has altered. The majority of countries rely heavily on agriculture, which needs to become “smart”. Recently, a lot of progress has been made, mostly in the fields of machine learning and data analysis, which help farmers make better decisions at every stage of the production process. Based on the analysis, the nutrients are organized and the best soil conditions for various regions are identified. The classification of the micronutrients present in soil is the primary objective of this study. This study thoroughly examines a range of soil micronutrients, such as phosphorus, nitrogen, sulfur, potassium, copper, boron, and others, to enhance crop recommendation systems. It looks at the several databases, algorithms, and soil analysis components used in precision farming. Therefore, this approach uses an examination of soil health and machine learning to classify micronutrients for crop suggestion.