Climate change has had a negative impact on the performance of most crops in India over the previous two decades. Crop yield prediction ahead of time would assist farmers and policymakers in determining appropriate marketing, transportation, and storage strategies. This proposed method will assist farmers in determining the crop yield prior to the cultivation of the agricultural land, allowing them to make informed decisions. In this work, first identify the factors that influence crop output to effectively predict the yield. Temperature, soil moisture, humidity, solar radiation, and pH value are all important factors. Needed to collect and analyze data on these factors for our benefit and there are various ways or algorithms for such data analysis in crop prediction, and can predict the crop yield with the help of these algorithms. In this proposed method, would like to look at the problem from the perspective of Machine Learning by evaluating various algorithms such as Random Forest, Simple Linear Regression (SLR), and Neural Networks to guarantee that considered the best algorithm and achieve the highest possible accuracy.