Improved drinking water, sanitation, and hygiene amenities are virtually unavailable in the majority of India's locations. We suggest the new combination Machine Learning (ML) approaches of K-Means clustering algorithm with the Support vector machine (SVM). This proposed method is used for the access of water and analysis of hygiene prediction on resource data using this suggested machine learning technique. This newly created hybridized K-Means+SVM algorithm gives the better overall performances, and it achieves the best output results. For this comparison purpose, we are taking the current models used in the water access and hygiene prediction is, Decision Tree, Logistic regression, and KNN. The overall performances between these existing and proposed methods are measured by using the performance evaluation parameters such as, accuracy, sensitivity, recall, precision, and time duration. Our newly constructed K-Means+SVM method obtained the accuracy of 94.70% with the less time duration of 20.63 ms. This method will be most widely suitable for quickly access for water level and hygiene prediction on resource data using the different machine learning methods in future.