Agriculture sector works as the source of raw material for non-Agricultural sectors. As much as 60% of the land is used for farming in India. It feeds around total of 1.2 billion in population. The population has also been increasing day to day and also the agriculture is not able to meet the demanded requirements for the increasing population. Crop yields, meanwhile, have already begun to suffer as a result of climate change. Unnatural climate changes can have a negative impact on food production and forecasting, which in turn can affect farmers' economics by resulting in low yields. Droughts, floods, heat waves, storms, and various more extreme weather events can destroy crops and reduce harvests, and climate change can bring about these conditions. Crop output may also be impacted by variations in temperature and rainfall patterns, which may change when crops are planted and harvested. These changes can make it difficult for farmers to predict their yields accurately, leading to economic losses. It is possible to produce precise crop prediction results by using the right parameters, such as soil nutrients properties (Nitrogen, Phosphorous, and Potassium, nutrients concentration, soil type and pH value), rainfall patterns, temperature patterns, soil structures, and other factors, such as crop diseases. A crucial aspect of agriculture is determining the best crop to grow, and in recent years, machine learning algorithms have become increasingly important in this process. This unique research is use of ML algorithms to better precisely recommend the crops based on the location. Supervised learning classification was used for the recomme ndations in this study. This study's main goal is to identify the most effective feature selection and classification techniques to predict the best harvest that will thrive in a particular environment, including temperature, rainfall, and geographic location in a given state, soil properties, including phosphorus (P), potassium (K), nitrogen (N), and pH value, as well as soil type. To provide recommendations for crops that are likely to thrive in a particular environment based on the available soil nutrients, an agronomic advisor application can be developed. The application can use a suitable classification algorithm to identify the most relevant features of the soil and environment. This can help farmers optimize their crop production and maximize their yields