Sleep is necessary for human health, although its specific physiological purpose is uncertain. Breathing dynamics are a critical diagnostic tool in therapeutic settings such as sleep analysis, intensive care, and central nervous and physiological disease analysis. Sleep apnea occurs when airflow to the lungs is interrupted for 10 seconds or more during sleep, often due to a loss of neuronal input from the central nervous system (Central Sleep Apnea) or upper airway collapse. (Obstructive Sleep Apnea). To solve this issue, a microcontroller-based sleep apnea monitor has been created. A respiration sensor monitors breathing conditions, a pulse sensor and SPO2 track heart rate and blood oxygen saturation, a digital humidity and temperature sensor, and a microphone detects snoring sounds are all included in the device. The microcontroller continuously checks all data, and if any breathing issues are identified, the patient's support system is activated immediately. This helpful technique assists patients in achieving consistent breathing, which can lessen or eliminate snoring, The system also includes manual and automatic modes, giving users more freedom and control over how the system behaves. While the manual mode enables users to change the settings and get feedback based on their particular needs, the automated mode may be utilized to continuously monitor and adjust the air pressure. Healthcare providers can assess patient progress and change treatment parameters as necessary with the help of IoT-based sleep apnea prevention and detection systems. For patients who reside in remote or underdeveloped locations or who have limited access to specialized medical care, this can be especially beneficial. The potential to improve the quality of life for the millions of people worldwide who are plagued by this sleep condition exists in the field of IoT-based sleep apnea prevention and detection. The continuous research and development in this field has the potential to fundamentally alter how sleep apnea is identified, treated, and managed, leading to better health outcomes and an overall improvement in quality of life.The principal aim of this paper is to provide a compact device and user-friendly app integrated into the device. The future scope is to increase the feasibility of the system by including the key concepts of machine learning for user’s accurate tracking