Artificial Intelligence in Bioremediation and Environmental Management

Abstract Bioremediation has proven to be a viable technique to combat pollution caused by hazardous pollutants, including petroleum hydrocarbons and xenobiotics, though conventional approaches have disadvantages such as heavy metal accumulation and low partition coefficients. This review focuses on incorporating artificial intelligence (AI) in bioremediation to improve microbial and site selection, pollutant screening, detection, and monitoring, thereby increasing process efficiency. Real‐time monitoring and management have been enabled by techniques like machine learning and predictive modeling, while AI applications in in situ, ex situ, phytoremediation, and mycoremediation strategies have enhanced pollution degradation. This review selectively points to implementation frameworks in a real‐world setting, regional case studies, and severe limitations to scalability. It presents a comparative analysis of AI models like artificial neural networks, random forests, and so forth, evaluating their strengths and limitations across different environmental settings. Further, new opportunities from combining AI with synthetic biology are discussed for designing microorganisms capable of degrading complex pollutants. Scalability challenges such as computational constraints and data availability are addressed alongside emerging solutions like cloud‐based platforms and open‐source AI tools. AI engagement will help diversify bioremediation into sustainable, adaptive processes with reduced health risks. This work integrates model‐level comparisons, AI–synthetic biology synergies, and field‐level challenges, offering a comprehensive perspective on data‐driven strategies beyond static, conventional remediation methods.