Environmental pollutionEnvironmental pollution poses significant challenges to public health and well-being. Accurate prediction and monitoring of pollution levels are essential to mitigate its adverse effects. Recent advancements in machine learningMachine learning (ML) and deep learningDeep learning (DL) techniques have revolutionized predictive modelling by enabling robust analysis of complex and high-dimensional environmental data. The present study provides a comprehensive examination of ML and DL applications in predicting various pollution types including air, water, noise, and soil pollutionSoil pollution. Key ML and DL methodologies and their performance metrics are discussed to highlight their effectiveness and limitations. ML techniques such as regression modelsRegression models, decision treesDecision trees, and ensemble methodsEnsemble methods offer simplicity and efficiency while DL architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing spatial and temporal data. Integration with Internet of ThingsInternet of Things (IoTIoT) devices for real-time monitoring and the emergence of explainable AIExplainable AI (XAIArtificial Intelligence (AI)) further enhance the applicability of these models.