In the context of global crop production, India stands prominently within the top trio of nations. Its farming community holds a pivotal position within the agricultural industry, yet a significant portion grapples with societal disadvantages. Despite the current scarcity of technical options, because there are so many different types of soil in the world, choosing the most lucrative crop is still difficult for many Indian farmers. This paper provides an improved precision farming approach through prediction of soil, crop, and disease in plants which uses machine learning to predict the optimal crop depending on a variety of variables, such as geography, soil type, yield, selling price, and more. Latest Learning techniques are separately utilized for soil, crop, and plant disease prediction. A comparison of these techniques is made to identify the better classifiers for soil and crop prediction.