Machine learning (ML) is revolutionizing environmental sciences, particularly in the fields of bioremediation and pollutant detection. Bioremediation, which utilizes biological organisms to neutralize contaminants, is increasingly benefiting from ML's predictive analytics capabilities to enhance the efficiency of microbial and enzymatic processes. By processing and analyzing large environmental datasets, ML algorithms can identify patterns in pollutant types, predict pollutant spread, and optimize microbial action, leading to tailored bioremediation strategies for specific environmental conditions. We provide a structured, scoping review of advances (2018–2024) in deploying machine learning (ML) for bioremediation and pollutant detection. Using a PRISMA-inspired approach based on a targeted search in Google Scholar, we applied a clearly defined query and manually screened studies for relevance, quantitative rigor, and real-world environmental validation. A total of 50 peer-reviewed articles met our inclusion criteria. We synthesize algorithmic workflows from data preprocessing to model interpretability and present comparative performance tables, and illustrate real-world implementations, including a case study on diesel degradation prediction using artificial neural networks. We also critique challenges such as data heterogeneity and model transferability and offer targeted solutions such as GAN-based data augmentation and containerized ML modules for integration into Transport Management Systems (TMS). All methods and metrics discussed are extracted directly from the published results of the included studies.