Aim: In the study, K-Nearest Neighbor Algorithm (KNN) is to predict climatic parameters of the crop and its performance is tested by comparing it with the k-nearest neighbor algorithm for better accuracy. Materials and Methods: Support vector machine learning algorithm with sample size n=10 and linear regression algorithm with sample size n= 10 with G-power analysis value of 80%. The crop based cultivation Novel prediction helps to improve the predicted accuracy. 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. Results: Average accuracy of 92% for linear regression algorithm and 82% for svm to predict customers' sales. Significant difference between Support vector machine and linear regression p<0.03 and with a significance value 0.02. Conclusion: Within the limits of study we found that predicting the novel crop based cultivation yields using natural parameters by using linear regression is better than predicting the climatic parameters by using K-nearest neighbor (KNN).