$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
Digital lending and fintech innovations have upended established banking systems, changing financial inclusion and credit availability in nations around the world. This study examines how peer-to-peer (P2P) and digital lending platforms are changing, emphasizing how technologies like artificial intelligence and machine learning are changing the way loans are approved. A thorough study of the literature highlights the opportunities and problems in the digital lending ecosystem, such as algorithmic risk assessment, customer trust, financial exclusion, and regulatory loopholes. This paper suggests a strong machine learning approach that uses a stacking ensemble model to accurately forecast loan approvals in order to address these issues. The data was pre-processed using train-test partitioning, exploratory analysis, and label encoding using a publicly accessible Kaggle dataset that included applicant demographics, financial characteristics, and credit histories. With XGBoost serving as the meta-learner, the ensemble incorporates the Gradient Boosting Model, Efficient Gradient Boosting, AdaBoost, and Extra Trees classifiers as base learners. With an accuracy of 98%, the model was assessed using measures including accuracy, precision, recall, F1-score, and error metrics (MAE- Mean Absolute Error, MSE- Mean Squared Error, and RMSE- Root Mean Square Error). According to correlation studies, factors including assets, income, and CIBIL scores have a significant impact on loan approvals. Outperforming conventional methods, the model showed balance and generalization across both classes. The usefulness of these models for automated, data-driven credit determinations is emphasized in the paper's conclusion.