Exploring machine learning approaches to enhance the effectiveness and precision of procedures related to bank loan approval. This investigation encompasses various methods such as logistic regression, decision trees, linear regression, as well as GaussianNB, Random Forest, and SVM. Utilizing a substantial dataset containing past loan applications and diverse applicant attributes like demographics, credit scores, income levels, and employment histories. The research endeavors to evaluate the recall, accuracy, precision, and F1-score metrics of various algorithms. Additionally, it investigates the interpretability and transparency of machine learning models to offer further insight into the variables affecting decisions on loan acceptance. The study emphasizes the efficacy of logistic regression, which outperformed SVM (77%), GaussianNB (78%), random forests (78%), and decision trees (69%), achieving the highest accuracy of 80% in loan approval. By implementing this model, we can enhance ML-driven loan approval processes within the banking industry, thereby elevating decision-making standards and enhancing consumer satisfaction.