By evaluating model performance in identifying effective treatments for orphan diseases, this research investigates data-driven drug repurposing. Through machine learning and biological data, the research seeks to enhance drug discovery efficiency, reduce costs, and hasten therapeutic development for under-served patient populations. The present study will investigate data-driven drug repurposing through network methods, machine learning, and natural language processing to identify possible cures for orphan diseases. To enhance prediction accuracy, it combines omics data, molecular docking, and virtual screening. Real-world data analysis is a validation method that guarantees model validity and speeds therapeutic discovery while lowering research costs. The results demonstrate how machine learning, network analysis, and omics integration can enhance drug repurposing accuracy for orphan diseases. Outcomes tag top repurposing candidates with high biological relevance, supported by molecular docking and experimental data, to ensure trustworthiness and enable treatment development within a shorter period. Drug repurposing is fueled by data-driven approaches through enhanced prediction accuracy, drug-target interaction validation, and speeding up orphan disease treatment, this research finds. In conclusion, the research finds predictability-enhanced, development-time-reduced data-driven approaches an essential force in repurposing orphan diseases. By adding omics information, network biology, and machine learning to the mix, candidate drugs are now more easily identifiable, which results in accelerated therapeutic development and improved health solutions.