Review of Machine Learning Algorithms for Solving Complex Engineering and Environmental Problems

Abstract

Machine learning (ML) has become a transformative computational paradigm for addressing the increasing complexity of engineering and environmental systems. As these systems generate vast, noisy, and heterogeneous datasets, traditional modelling approaches often fail to capture nonlinear relationships and dynamic interactions. This review synthesises major machine learning algorithms applied to engineering optimisation, structural analysis, transportation modelling, climate prediction, hydrological assessment, pollution forecasting, and ecosystem monitoring. It evaluates supervised, unsupervised, reinforcement, and deep learning methods, with emphasis on algorithmic performance, computational efficiency, and real-world applicability. The review highlights the ability of ML models to uncover hidden patterns, improve predictive accuracy, and support real-time decision-making in large-scale applications. Fabrication-equivalent computational methodologies, including data preprocessing, feature engineering, model validation, and hybrid modelling frameworks, are examined for their role in enhancing robustness. Environmental applications such as flood forecasting, groundwater modelling, air-quality prediction, and remote sensing-based land-use classification demonstrate ML’s expanding significance in sustainable development. Engineering applications include fault detection, structural health monitoring, design optimisation, and intelligent control systems. Challenges involving data scarcity, model interpretability, transferability, and ethical considerations are critically analysed. The review concludes that ML will remain central to next-generation engineering and environmental solutions, especially when integrated with physics-informed models, IoT systems, and high-performance computing.