This comprehensive review by Justus A. Ilemobayo and colleagues explores the critical role of hyperparameter tuning in optimizing the performance and generalization of machine learning (ML) models. The authors discuss key factors influencing ML performance, such as data quality, algorithm selection, and model complexity, and highlight the impact of hyperparameters like learning rate and batch size on model training. Various tuning methods, including grid search, random search, Bayesian optimization, and meta-learning, are examined in detail. Special attention is given to the learning rate in deep learning, with strategies for its optimization. The review also addresses trade-offs in hyperparameter tuning, such as balancing computational cost and performance gain. Finally, it concludes with challenges and future directions, providing a resource for improving the effectiveness and efficiency of ML models. The review emphasizes the importance of hyperparameter tuning in achieving high performance, which is crucial for operational efficiency, competitive advantage, and research advancement in various fields, including healthcare, finance, agriculture, and autonomous systems.This comprehensive review by Justus A. Ilemobayo and colleagues explores the critical role of hyperparameter tuning in optimizing the performance and generalization of machine learning (ML) models. The authors discuss key factors influencing ML performance, such as data quality, algorithm selection, and model complexity, and highlight the impact of hyperparameters like learning rate and batch size on model training. Various tuning methods, including grid search, random search, Bayesian optimization, and meta-learning, are examined in detail. Special attention is given to the learning rate in deep learning, with strategies for its optimization. The review also addresses trade-offs in hyperparameter tuning, such as balancing computational cost and performance gain. Finally, it concludes with challenges and future directions, providing a resource for improving the effectiveness and efficiency of ML models. The review emphasizes the importance of hyperparameter tuning in achieving high performance, which is crucial for operational efficiency, competitive advantage, and research advancement in various fields, including healthcare, finance, agriculture, and autonomous systems.