The study in this research introduces a unified framework which could assist in precision agriculture through effective predictive analytics of water quality monitoring data and also the localization of water quality monitoring sensors. It adopts analytical baseline to realize the proposed concept in the domain of wireless sensor network. The architectural design of the proposed system initially handles the redundancy of the data and performs data visualization followed by a preliminary analysis. The unified framework evaluates both classification and regression modeling to perform predictive analytics considering i) Logistic Regression Model , ii) KNN , iii) Random Forest and iv) Gaussian NB Classifier. The prime objective of this ML based solution is to accurately evaluate the reliability of the sensor data towards monitoring and assessing the water quality. This also indicates whether the sensor nodes are localized correctly or not. For this reason a set of performance metrics are considered for validation which are accuracy, precision, recall rate and F1_Score. The experimental outcome obtained from the simulation shows that the framework provides significant insight about the suitability of the reliable learning model among the four ML based approaches for predictive analytics on sensor node placement strategy. It also verifies the reliability of the water quality measurement sensor data with assessment of accuracy.