In the field of machine learning, there is a wide number of approaches that may be used to develop a model that is capable of anticipating data that has not yet been seen and to learn prediction rules based on historical data. Machine learning involves analysing data samples in order to recognise patterns and determine how to make decisions. This is done with the goal of predicting future data. The current model of agriculture, known as "smart agriculture," views a farm as a collection of distinct units and searches for inconsistencies in the levels of output and demand at each individual unit. The ultimate goal of "smart farming" is to reduce overhead costs while simultaneously increasing crop yields and business revenue. Farmers who are on the cutting edge of agricultural practises employ them. The predictive power of machine learning algorithms paves the way for the implementation of intelligent agricultural practises.