This paper focuses on the application of machine learning techniques and time series models such as ARIMA, SARIMA, and PROPHET method for the detection and analysis of physical crimes. The study explores the use of machine learning algorithms and time series models to predict crime rates based on historical data, identify patterns and trends in crime data, and ultimately assist law enforcement agencies in preventing and solving physical crimes. The research methodology involves analyzing crime data from various sources, preprocessing the data to identify relevant features, and building and testing predictive models using machine learning and time series analysis techniques. The results of the study demonstrate the effectiveness of these methods in predicting crime rates and identifying trends in physical crime data. The findings of this research could have significant implications for law enforcement agencies, policymakers, and researchers in the field of criminology. By using machine learning and time series analysis techniques, physical crime prevention and detection can be improved, ultimately leading to a safer and more secure society.