This study critically reviews the application of data-driven modeling techniques in organic waste treatment, focusing on methods such as neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors. The review highlights the capacity of these techniques to optimize complex processes and integrate domain knowledge through physics-informed neural networks. Comparative analyses are conducted to provide insights into the strengths and weaknesses of each technique, aiding practitioners in selecting appropriate models for diverse applications. The study also discusses transfer learning and specialized neural network variants to enhance predictive capabilities. It emphasizes the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios. The review covers a range of organic waste treatment technologies, including gasification, pyrolysis, hydrothermal treatment, anaerobic digestion (AD), composting, and dark fermentation. Each technology is described in detail, along with its process dynamics, challenges, and potential applications. The integration of ML-based modeling with environmental impact assessment models is also addressed, along with strategies to overcome challenges in popularizing ML-based biological process modeling. The paper outlines the development pipeline for ML-based models, including data collection, preprocessing, normalization, dataset splitting, dimensionality reduction, feature importance analysis, and model performance evaluation. It provides a comprehensive overview of the predictor and target variables used in ML-based modeling of organic waste treatment. Key ML models discussed include neural networks (FNN, RNN-LSTM, CNN, transformers), physics-informed neural networks (PINNs), support vector machines (SVM), decision trees (DT, ensembled DT), generalizable linear models (GLMNET), Gaussian process regression (GPR), and k-nearest neighbors (KNN). Each model's architecture, training process, and applications in organic waste treatment are detailed. State-of-the-art applications of ML methods in thermochemical technologies, such as gasification and pyrolysis, are reviewed. These applications often combine ML models with optimization algorithms to solve goal-oriented optimization problems. The review highlights the use of feature importance and partial dependence analysis to enhance model interpretability and gain deeper insights into process dynamics. Overall, the study contributes valuable insights to the field of data-driven modeling, emphasizing the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios.This study critically reviews the application of data-driven modeling techniques in organic waste treatment, focusing on methods such as neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors. The review highlights the capacity of these techniques to optimize complex processes and integrate domain knowledge through physics-informed neural networks. Comparative analyses are conducted to provide insights into the strengths and weaknesses of each technique, aiding practitioners in selecting appropriate models for diverse applications. The study also discusses transfer learning and specialized neural network variants to enhance predictive capabilities. It emphasizes the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios. The review covers a range of organic waste treatment technologies, including gasification, pyrolysis, hydrothermal treatment, anaerobic digestion (AD), composting, and dark fermentation. Each technology is described in detail, along with its process dynamics, challenges, and potential applications. The integration of ML-based modeling with environmental impact assessment models is also addressed, along with strategies to overcome challenges in popularizing ML-based biological process modeling. The paper outlines the development pipeline for ML-based models, including data collection, preprocessing, normalization, dataset splitting, dimensionality reduction, feature importance analysis, and model performance evaluation. It provides a comprehensive overview of the predictor and target variables used in ML-based modeling of organic waste treatment. Key ML models discussed include neural networks (FNN, RNN-LSTM, CNN, transformers), physics-informed neural networks (PINNs), support vector machines (SVM), decision trees (DT, ensembled DT), generalizable linear models (GLMNET), Gaussian process regression (GPR), and k-nearest neighbors (KNN). Each model's architecture, training process, and applications in organic waste treatment are detailed. State-of-the-art applications of ML methods in thermochemical technologies, such as gasification and pyrolysis, are reviewed. These applications often combine ML models with optimization algorithms to solve goal-oriented optimization problems. The review highlights the use of feature importance and partial dependence analysis to enhance model interpretability and gain deeper insights into process dynamics. Overall, the study contributes valuable insights to the field of data-driven modeling, emphasizing the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios.