The ensemble learning approach is a cooperative decision-making system that generates new examples by combining the predictions of learnt classifiers. The early analysis has demonstrated that the ensemble classifiers are far more reliable than any single part classifier, both conceptually and experimentally. Even with the presentation of multiple ensemble approaches, determining the right configuration for a given dataset remains a challenging process. The goal of crime prediction is to discourage criminal activity and lower the crime rate. This paper presents the assemble-stacking based crime prediction method (SBCPM), an authentic and efficient approach based on algorithms for determining the right crime predictions. Learning-based methods are applied to achieve domain-specific configurations that are compared with another machine learning model. The ensemble model that has the highest correlation coefficient and the lowest average and absolute errors sometimes performs better than the other models. On the test set, the suggested approach produced accurate classifications. It was discovered that the suggested method was helpful in forecasting potential crimes and imply that the ensemble model's prediction accuracy is greater than the individual classifier's.