The proposed research work presents a deep learning convolutional neural network model personalized for the uncovering of snoring actions using audio signals. For a numeral of robust motives, snoring exposure is imperative in the framework of health care and sleep. Breathing concerns associated to sleep, such sleep apnea, are recurrently specified by snoring. Timely recognition and handling of these disorders can result in more successful interventions and better overall health outcomes. Leveraging advanced deep neural network architectures, the model is trained on a diverse dataset to accurately identify, predict and classify snoring patterns. This research presents an innovative and efficient deep learning model designed for the automated detection of snoring events in audio signal recordings. A convolutional neural network (CNN) architecture is used in the suggested model, involving various classifier method, leveraging its ability to learn hierarchical features from spectrogram representations of audio signals. The dataset used for training and evaluation encompasses a diverse range of snoring instances, capturing variations in pitch, intensity, and temporal patterns. Through rigorous training and validation processes, the deep learning model demonstrates high accuracy and robust generalization, effectively distinguishing between snoring and background noise. The evaluation metrics underscore the model’s reliability and effectiveness in real-world scenarios. The study contributes to the field of sleep technology by providing a sophisticated and scalable solution for snoring detection. The potential applications extend to personalized sleep interventions, telemedicine, and the broader landscape of digital health solutions.