A key component of effective delivery of healthcare is timely and accurate diagnosis. However, diagnostic errors continue to be a significant source of inefficiencies and adverse clinical outcomes. This study presents a machine learning-based Diagnosis Recommendation System designed to predict ICD-10 codes from free-text patient complaints. The main objective is to improve diagnostic precision by incorporating natural language processing and Naive Bayes classification into a scalable system. A synthetic dataset, structured to simulate realistic clinical scenarios, was used for training and testing. Text preprocessing, TF-IDF vectorization, and cross-validation formed the core of the methodology. Comparative evaluations with Support Vector Machines (SMO) confirmed the superior interpretability and reliability of Naive Bayes, especially in data-limited contexts. The system achieved consistent performance across department-specific models and offers practical integration into hospital systems, telemedicine platforms, and administrative coding workflows. While promising, the study also recognizes its limitations and outlines directions for future enhancement.