In large part, India's economy relies on the expansion of agricultural yields and the output of the agroindustrial sector because of the country's agricultural focus. The study of agricultural yields via the lens of data mining is a relatively new area of study. To accurately anticipate agricultural yields is a critical problem. Farmers care about two things: how much profit they can anticipate to make, and what kinds of crops will thrive on their land. Determine soil alkalinity by analyzing its location and pH level, for example. In addition, third-party applications, such as APIs for weather and temperature, soil type, nutrient value of the soil in that region, amount of rainfall in that region, and soil composition can be determined by using location and the percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K). For the purpose of developing a model, we will examine all of these data characteristics, as well as train the data using a number of different machine learning techniques, including SVM, Random-Forest, KNN, and Voting Classifier.