International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com K Varaprasad 3 2 (March - April) 2021 03 March-April enCrop Yield PredictionMachine LearningAgricultureData AnalyticsIndia Agriculture plays a vital role in Indias economy and accurate crop yield prediction is essential for ensuring food security efficient resource allocation and policy formulation Traditional prediction methods often fail to account for the complex interactions between climatic soil and management factors This paper presents a comprehensive survey on the use of machine learning ML techniques for predicting Indian crop yield Various supervised and unsupervised learning algorithmssuch as Linear Regression Random Forest Support Vector Machines SVM Decision Trees and Artificial Neural Networks ANNhave been analyzed for their performance in forecasting yield across diverse agroclimatic regions The survey highlights the importance of feature selection data preprocessing and the integration of remote sensing and weather data for improving prediction accuracy Additionally it discusses challenges such as data inconsistency regional variability and model generalization The study concludes that machine learningbased models offer a promising approach for building intelligent datadriven agricultural systems that support sustainable farming and policy planning in India Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2021 All Rights Reserved