This paper discusses hyperparameter optimization (HPO) in machine learning (ML) algorithms, focusing on both theoretical foundations and practical applications. It reviews common ML algorithms and their key hyperparameters, analyzes various HPO techniques, and provides an overview of popular HPO libraries and frameworks. The paper also discusses open challenges and research directions in HPO. Hyperparameters are parameters that are not learned from data but must be set before training a model. They influence the architecture and performance of ML models. Effective hyperparameter tuning is crucial for building high-performing ML models, especially for tree-based models and deep neural networks, which have many hyperparameters. Traditional optimization methods like gradient descent may not be suitable for HPO due to the non-convex and non-differentiable nature of many HPO problems. Instead, modern techniques such as Bayesian optimization, multifidelity optimization, and metaheuristics are more effective for HPO. Bayesian optimization (BO) is a popular HPO method that uses probabilistic models to guide the search for optimal hyperparameters. It can efficiently find optimal configurations with fewer iterations than grid search or random search. Multifidelity optimization techniques, such as Hyperband, are also effective for HPO, especially when resources are limited. Metaheuristics like genetic algorithms and particle swarm optimization are suitable for large search spaces with many hyperparameters. The paper discusses the key hyperparameters of common ML models, including linear models, KNN, SVM, Naïve Bayes, tree-based models, ensemble learning algorithms, and deep learning models. For each model, the paper identifies important hyperparameters that need to be tuned, such as regularization strength, kernel type, learning rate, and model architecture. It also provides practical examples of HPO and discusses the challenges and future directions in HPO research. The paper concludes that selecting the appropriate HPO method is essential for achieving optimal model performance and that further research is needed to improve HPO techniques for complex ML problems.This paper discusses hyperparameter optimization (HPO) in machine learning (ML) algorithms, focusing on both theoretical foundations and practical applications. It reviews common ML algorithms and their key hyperparameters, analyzes various HPO techniques, and provides an overview of popular HPO libraries and frameworks. The paper also discusses open challenges and research directions in HPO. Hyperparameters are parameters that are not learned from data but must be set before training a model. They influence the architecture and performance of ML models. Effective hyperparameter tuning is crucial for building high-performing ML models, especially for tree-based models and deep neural networks, which have many hyperparameters. Traditional optimization methods like gradient descent may not be suitable for HPO due to the non-convex and non-differentiable nature of many HPO problems. Instead, modern techniques such as Bayesian optimization, multifidelity optimization, and metaheuristics are more effective for HPO. Bayesian optimization (BO) is a popular HPO method that uses probabilistic models to guide the search for optimal hyperparameters. It can efficiently find optimal configurations with fewer iterations than grid search or random search. Multifidelity optimization techniques, such as Hyperband, are also effective for HPO, especially when resources are limited. Metaheuristics like genetic algorithms and particle swarm optimization are suitable for large search spaces with many hyperparameters. The paper discusses the key hyperparameters of common ML models, including linear models, KNN, SVM, Naïve Bayes, tree-based models, ensemble learning algorithms, and deep learning models. For each model, the paper identifies important hyperparameters that need to be tuned, such as regularization strength, kernel type, learning rate, and model architecture. It also provides practical examples of HPO and discusses the challenges and future directions in HPO research. The paper concludes that selecting the appropriate HPO method is essential for achieving optimal model performance and that further research is needed to improve HPO techniques for complex ML problems.