Environmental data management techniques for high-dimensional environmental data that contains multiple data sources and measurement inaccuracies. The research develops a hierarchical Bayesian framework to solve this problem by combining prior knowledge with observed data for creating reliable probabilistic predictions. The researchers used Gibbs and Hamiltonian sampling from Advanced Markov Chain Monte Carlo methods to determine posterior distributions and they evaluated model fit and predictive accuracy using the Deviance Information Criterion (DIC). The framework showed effective results in tracking air quality and climate indicators by revealing how temperature and wind speed affect pollution levels in different areas at various times. The Bayesian inference method proved superior to traditional frequentist methods because it delivered enhanced uncertainty assessment and superior prediction results. The research demonstrates how Bayesian methods can be applied to air quality monitoring and climate risk assessment and resource management which enables evidence-based sustainable environmental planning. The framework allows scientists to develop Bayesian models at scale for environmental science while establishing methods to study complex multidimensional data sets.