<![CDATA[The increasing complexity of healthcare systems highlights the need for data-driven approaches to optimize drug usage patterns and improve disease management. This study employs the K-Nearest Neighbors (KNN) algorithm to analyze correlations between prescribed medications and associated diseases, utilizing a dataset comprising attributes such as patient demographics, drug types, dosages, and treatment frequencies. The results reveal significant trends, including the predominance of "Drug_D" due to its versatility across multiple conditions such as hypertension, diabetes, and cardiovascular diseases. The study also highlights the prevalence of chronic conditions like hypertension and respiratory disorders, underscoring the importance of preventive healthcare and resource allocation. Simplified dosage regimens, predominantly "Once_Daily," were found to enhance patient adherence, aligning with global best practices in chronic disease management. The analysis further emphasizes targeted prescribing practices, with specific drugs strongly correlated to particular diseases, such as "Drug_A" for hypertension and "Drug_B" for respiratory disorders. However, the broad usage of certain medications raises concerns about potential over-reliance, necessitating regular monitoring. These findings demonstrate the value of machine learning in improving healthcare decision-making, enhancing operational efficiency, and supporting evidence-based practices. Future research should expand the dataset to include genetic and lifestyle factors to further refine predictive accuracy and contribute to the advancement of personalized medicine. This study underscores the transformative potential of integrating data mining techniques into healthcare systems to achieve better patient outcomes and more effective resource management.]]>
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