Aim: In this study, K-Nearest Neighbors algorithm (KNN) is used to predict climatic parameters of the crops and its performance is tested by comparing it with the Random Forest algorithm for the crop-based cultivation for better accuracy. Materials and Methods: The Crop-based on Climatic Parameters consists of 3101 of different crops and climatic parameters used for training 3000 (80%) and testing 101 (20%) the predictive model in python and the statistical analysis is done using SPSS software. The sample size is estimated using G power to be 3101 records in each group with 80% of power and a 0.05 Error rate. K-Nearest Neighbors (KNN) algorithm is used and compared with Random Forest algorithm. Results: The K-Nearest Neighbors algorithm’s predictive model shows a higher accuracy of 86.61% than the Random Forest based model with an accuracy of 93.54% and with a significance value 0.010 (p<0.05). Conclusion: Within the limits of study confirms that the Random Forest based model provides more promising results in the Crop-based cultivation on Climatic parameters than the K-Nearest Neighbors (KNN) based model.