Predicting loan eligibility in the evolving financial technology continuously becomes essential to make lending decisions more efficient and balanced. The proposed system utilizes Random Forest algorithm to forecast loan acceptance decisions from various applicant characteristics which include income amount and credit record history and employment details and requested loan sums. The system consumes genuine data which processes categorical and continuous variables to generate dependable predictions from a collective set of decision trees. The evaluation of the model incorporated standard classification metrics which consisted of accuracy, precision, recall and F1-score. Experimental data proves that Random Forest surpasses standard modeling approaches such as logistic regression and decision trees and effectively resists overfitting together with data instability. The results from feature importance analysis show which factors most significantly affect loan decisions thus helping explain models decisions to users. The system provides adjustable automation for banks which needs streamlining lending operations alongside reduced errors and discrimination. The study demonstrates how machine learning can develop traditional financial analysis into smarter and data-centric assessment methods.