This study enfolds and relies on explainable AI and Machine Learning Algorithms to understand the many categories of Electric Vehicle Charging Stations Available in India. Single Classifier Techniques such as Logistic Regression, KNN, SVM, Decision Tree and Random Forest Classification were done and its accuracy and precisions were derived, out of which Decision Tree Classifier has the highest precision of 95%. Ensembling models were also implemented with Bagging and Boosting Techniques where the Gradient Boost stems out a precision of 95% and Random Forest classifier bears a precision of 98%. Harnessing of XAI methods such as LIME and SHAP, provides a comprehensive understanding of feature importance, feature contribution and local explanation. This research’s significance lies in providing the capacities of the EV stations, bestowing the precision, accuracy, recall and F1. The combination of Machine learning techniques and XAI contributes to advanced screening tools, foresting trust among ecologist and researchers.