This survey article provides an in-depth examination of methods for detecting change points in time series data. Change points are abrupt variations that can represent transitions between states, and their detection is crucial in various applications such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. The article categorizes and compares both supervised and unsupervised algorithms used for change point detection, introducing several criteria to evaluate their performance. Key concepts and definitions are provided, including the formal introduction of change point detection, the distinction between online and offline algorithms, and the importance of scalability and algorithm constraints. Performance evaluation metrics, such as accuracy, sensitivity, G-mean, precision, F-measure, ROC, PR curve, MAE, MSE, MSD, RMSE, and NRMSE, are discussed to assess the effectiveness of different algorithms. The review covers a range of supervised and unsupervised methods, including likelihood ratio methods, subspace model methods, probabilistic methods, kernel-based methods, graph-based methods, and clustering methods. Each method is described in detail, highlighting its strengths and limitations. The article concludes by discussing grand challenges and future research directions in the field of change point detection.This survey article provides an in-depth examination of methods for detecting change points in time series data. Change points are abrupt variations that can represent transitions between states, and their detection is crucial in various applications such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. The article categorizes and compares both supervised and unsupervised algorithms used for change point detection, introducing several criteria to evaluate their performance. Key concepts and definitions are provided, including the formal introduction of change point detection, the distinction between online and offline algorithms, and the importance of scalability and algorithm constraints. Performance evaluation metrics, such as accuracy, sensitivity, G-mean, precision, F-measure, ROC, PR curve, MAE, MSE, MSD, RMSE, and NRMSE, are discussed to assess the effectiveness of different algorithms. The review covers a range of supervised and unsupervised methods, including likelihood ratio methods, subspace model methods, probabilistic methods, kernel-based methods, graph-based methods, and clustering methods. Each method is described in detail, highlighting its strengths and limitations. The article concludes by discussing grand challenges and future research directions in the field of change point detection.