Pollution is a critical global issue, adversely affecting the environment, human health, plants, and animals. Microbial degradation of pollutants and toxins offers a promising strategy for environmental remediation. Optimizing these processes requires a thorough understanding of complex microbial interactions and environmental factors. This chapter delves into the sources and impacts of pollutants and toxins, alongside their microbial degradation using advanced techniques such as artificial intelligence (AI) and machine learning (ML). It is valuable for environmental scientists, biogeochemists, and industrial practitioners aiming to manage polluted sites and mitigate toxin presence. Further, it provides a succinct overview of how AI and ML are revolutionizing microbial degradation studies to enhance pollutant removal. AI and ML provide robust tools for predictive modelling and process optimization in microbial degradation. ML algorithms identify optimal conditions for microbial activity, thereby improving bioremediation efficiency. Furthermore, AI-driven decision support systems enable dynamic adjustments to environmental conditions, maximizing pollutant removal. In conclusion, integrating AI and ML with microbial degradation holds great potential for advancing environmental remediation. Through computational modelling and data-driven insights, researchers can optimize microbial degradation strategies to address complex pollutants, contributing to sustainable environmental management and ecosystem restoration.