This review critically examines the application of machine learning (ML) in sustainable organic waste treatment, focusing on data-driven modeling techniques for optimizing processes in thermochemical and biochemical waste treatment technologies. The study explores various ML methods, including neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors, to predict process outcomes, optimize parameters, and enhance control systems. Physics-informed neural networks (PINNs) are highlighted for integrating domain knowledge into ML models, improving consistency and accuracy. Comparative analyses of these techniques provide insights into their strengths and weaknesses, aiding practitioners in selecting appropriate models for different applications. Transfer learning and specialized neural network variants are also discussed to enhance predictive capabilities. Organic waste treatment technologies, such as gasification, pyrolysis, hydrothermal treatment, anaerobic digestion, composting, and dark fermentation, are reviewed, emphasizing their roles in resource recovery and reducing greenhouse gas emissions. These technologies involve complex processes that require precise control and optimization, which can be challenging due to the variability of feedstock and process conditions. Traditional kinetic and thermofluidic models are computationally intensive and less feasible for real-time applications, making data-driven ML models a promising alternative. ML-based models offer benefits such as faster computation, reduced recalibration, and the ability to embed physical laws within the framework. They are used to predict product yields, process stability, and environmental impacts, aiding in the optimization of waste treatment processes. The study also addresses challenges in popularizing ML-based biological process modeling, including data scarcity, model interpretability, and integration with existing systems. Potential mitigation strategies are discussed to enhance the adoption and effectiveness of ML in organic waste treatment. The review highlights the importance of understanding the nuances of each ML technique for informed decision-making in various organic waste treatment scenarios. It emphasizes the need for comprehensive, up-to-date summaries of waste treatment technologies and their associated ML applications to guide future research and development in this field. The integration of ML with environmental impact assessment models is also explored to support sustainable waste management practices. Overall, the study underscores the potential of ML in advancing the efficiency, sustainability, and effectiveness of organic waste treatment technologies.This review critically examines the application of machine learning (ML) in sustainable organic waste treatment, focusing on data-driven modeling techniques for optimizing processes in thermochemical and biochemical waste treatment technologies. The study explores various ML methods, including neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors, to predict process outcomes, optimize parameters, and enhance control systems. Physics-informed neural networks (PINNs) are highlighted for integrating domain knowledge into ML models, improving consistency and accuracy. Comparative analyses of these techniques provide insights into their strengths and weaknesses, aiding practitioners in selecting appropriate models for different applications. Transfer learning and specialized neural network variants are also discussed to enhance predictive capabilities. Organic waste treatment technologies, such as gasification, pyrolysis, hydrothermal treatment, anaerobic digestion, composting, and dark fermentation, are reviewed, emphasizing their roles in resource recovery and reducing greenhouse gas emissions. These technologies involve complex processes that require precise control and optimization, which can be challenging due to the variability of feedstock and process conditions. Traditional kinetic and thermofluidic models are computationally intensive and less feasible for real-time applications, making data-driven ML models a promising alternative. ML-based models offer benefits such as faster computation, reduced recalibration, and the ability to embed physical laws within the framework. They are used to predict product yields, process stability, and environmental impacts, aiding in the optimization of waste treatment processes. The study also addresses challenges in popularizing ML-based biological process modeling, including data scarcity, model interpretability, and integration with existing systems. Potential mitigation strategies are discussed to enhance the adoption and effectiveness of ML in organic waste treatment. The review highlights the importance of understanding the nuances of each ML technique for informed decision-making in various organic waste treatment scenarios. It emphasizes the need for comprehensive, up-to-date summaries of waste treatment technologies and their associated ML applications to guide future research and development in this field. The integration of ML with environmental impact assessment models is also explored to support sustainable waste management practices. Overall, the study underscores the potential of ML in advancing the efficiency, sustainability, and effectiveness of organic waste treatment technologies.