Real-time monitoring of breath biomarkers such as ammonia, alcohol, and volatile organic compounds (VOCs) is important for early disease diagnosis of metabolic and organ-related diseases. Traditional disease diagnosis techniques are invasive, time-consuming, expensive and not suitable for using in remote areas. To overcome these limitations, this paper proposes the design and development of an IoT-based Smart Breath Analyzer for real-time monitoring and analysis of ammonia, alcohol, and volatile organic compounds (VOC) concentration present in exhaled human breath. The system consists of different kinds of gas sensors connected to an ESP32 microcontroller for measuring gas concentration, which is processed and sent to Blynk application via Wi-Fi for visualization and disease prediction. A trained machine learning model is used in the system which classifies biomarker patterns that may be associated with conditions such as kidney disorders, respiratory issues, or alcohol influence, based on literature-derived thresholds. The system is presented as a proof-of-concept screening tool rather than a clinical diagnostic solution. The results are showed on an OLED display and accessed via a mobile app developed using the Blynk IoT platform. This non-invasive, affordable, and scalable solution improves continuous health monitoring and early diagnosis.