Farmers are producers of almost every basic need of human. But due to lack of knowledge about impact of growing same crop, inadequate use of fertilizers, soil erosion, and climatic challenges, etc., they come to face loss that also affect whole economy as farmers are prime producers. In order to optimize resource allotment, safety of foodstuffs, climate challenges, variations in crops, and predict booming market the agricultural sector relies heavily on accurate crop prediction. Involvement of Machine Learning offers advanced algorithms that majorly enhance crop prediction accuracy. In order to make predictions diverse data sources like weather, soil type, temperature, humidity, climatic changes and historical data of crops are required. This paper introduces a comparative analysis of various Machine Learning algorithms used for crop prediction. The paper particularly focuses on two algorithms with maximum accuracy among all, namely “Long Short-Term Memory” (LSTM) and “Random Forest” designed to handle sequential or time-series data. The goal is to find out best suited algorithm, for that the further study evaluates the parameters of performance metrics such as accuracy, precision, and computational complexity. Results show that LSTM outperforms other models in time-series crop prediction, delivering superior accuracy. The paper includes facts and figures to support the findings.