Sleep apnea, a prevalent sleep disorder characterized by recurrent episodes of upper airway obstruction during sleep, poses significant health risks, including cardiovascular complications, cognitive impairment, and increased mortality. The timely and accurate detection of sleep apnea is crucial for initiating appropriate treatment and mitigating these adverse outcomes. Traditional methods for sleep apnea diagnosis, such as polysomnography, are resource-intensive, time-consuming, and often inconvenient for patients. Consequently, there is a growing need for automated and accessible sleep apnea detection techniques that can be readily deployed in clinical and home settings Deep learning approaches have emerged as promising tools for analyzing physiological signals and identifying complex patterns indicative of sleep apnea. Leveraging the power of deep learning, researchers are developing innovative solutions to improve the accuracy and efficiency of sleep apnea detection, ultimately leading to better patient care and management. This research explores the application of deep learning techniques, specifically Long Short-Term Memory networks and VGG networks, for the automated detection of sleep apnea using physiological signals. The proposed approach aims to leverage the temporal dependencies captured by LSTM networks and the feature extraction capabilities of VGG networks to develop a robust and accurate sleep apnea detection system. The utilization of wearable sensor data presents a non-invasive and convenient method for monitoring individuals, athletes, and high-risk patients in real-time.