In Pakistan, crime is always a significant economic and social issue affecting governance and public safety. Social issues, poverty, and political instability have a very high contribution to increasing crime in the country. Despite highly effective efforts of authorities and prevention techniques, crimes are often inconsistent and lack effectiveness in determining crimes across the country. This study aims to analyze crimes in Pakistan using machine learning and structured datasets to support more effective decision-making and a deeper understanding of crime rates nationwide. The machine learning techniques, including linear regression, decision trees, and random forests, are applied to the Pakistan crimes dataset (2012–2017) obtained from Kaggle. With different evaluation matrices and cost functions, the proposed techniques were interpreted and evaluated. The Evaluation metric includes the R2 score, and the cost functions include MSE, MAE, and RMSE. Linear Regression performed effectively in comparison to other techniques, yielding the highest R2 score value of 99.9%. By using this framework, a predictive analysis of crimes from 2030 to 2050 was conducted with a 5-year interval. These algorithms help to identify different types of crimes across the country based on different factors. Unlike traditional methods and black-box models, this research emphasizes comprehensibility, making insights accessible and understandable. The study explains how interpretable frameworks can help authorities to find high-risk area predictions. In the future, the aim is to utilize deep learning techniques in conjunction with explainable AI (XAI) to enhance this basic study, thereby supporting the development of more effective public safety frameworks nationwide.