Loans are an integral part of the modern world, and a larger part of the profit that banks gain comes from loans. However, lending or not lending a loan is not so simple: many factors are kept in mind by the banks before lending a loan to an applicant. Machine learning enhances the conventional loan granting process by using the ensemble learning approach which incorporates algorithms from Random Forest, Gradient Boosting, Extra Trees, Logistic Regression, Decision Tree, and K-Nearest Neighbors. The methodology used comes with intense preprocessing techniques, such as missing value imputation, outlier detection, square root transformations, and application of the SMOTE technique to ensure proper class distribution. The dataset contains 16 features, Credit history and income level are the most important features for loan approval. The basis for feature selection and correlation analysis can be on this assumption. An ensemble method leads to 93.33% accuracy where individual model strengths are brought together to make more accurate decisions and fasten loan decisions. The overall interface is user-friendly, making it easier for stakeholders to interact with the prediction system. The method reduces the processing time and risk assessment within financial institutions, while high accuracy persists in predictions.