By incorporating Random Forest, KNN, ANN, and Logistic Regression techniques, our credit assessment model offers a comprehensive approach to analysing various aspects of creditworthiness. The ensemble learning of Random Forest, the neighbour-based classification of KNN, the deep learning capabilities of ANN, and the probability estimation of Logistic Regression work in harmony to capture complex relationships and make precise credit assessments. These techniques enable lenders to evaluate credit risk accurately, identify patterns in borrower behaviour, and make informed decisions on creditworthiness. Our model's ability to handle diverse data and capture non-linear relationships enhances the accuracy and robustness of credit assessments. Furthermore, by leveraging these techniques, our model can adapt and learn from new data, continuously improving its predictive capabilities. This empowers lenders to stay ahead in an evolving credit landscape, effectively managing risk and optimizing their lending practices. In summary, our ML-based credit assessment model integrates Random Forest, KNN, ANN, and Logistic Regression techniques to provide lenders with a sophisticated tool for accurate credit evaluations. Through the combination of these techniques, our model delivers a comprehensive analysis of creditworthiness, enabling lenders to make informed decisions and effectively manage their lending portfolios.