Respiratory ailments, particularly asthma, present significant healthcare challenges globally, necessitating prompt and precise identification for effective management. Conventional diagnostic techniques typically depend on clinical evaluations and pulmonary function assessments, which might be cumbersome and are often not available in isolated locations. This investigation delves into the use of artificial intelligence (AI) in monitoring respiratory health, with an emphasis on AI-enhanced asthma detection. By utilizing machine learning (ML) and deep learning (DL) methodologies, we scrutinize respiratory patterns, cough acoustics, and environmental influences to boost early diagnosis accuracy. The suggested approach incorporates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process audio signals and time-series data gathered from wearable devices. A dataset containing both clinical and real-world respiratory sound recordings was employed to train and fine-tune these models. We applied feature extraction methods, including spectrogram analysis and statistical pattern recognition, to heighten classification precision. The experimental outcomes confirm that the AI model surpasses traditional diagnostic approaches in detection accuracy. Performance metrics, including sensitivity, specificity, and F1-score, substantiate the system’s capability to pinpoint asthma symptoms accurately. The results of this exploratory work illustrate the significant potential AI-driven systems hold for real-time asthma monitoring, facilitating timely interventions and decreasing the need for hospital visits. Incorporating AI with IoT-based wearable technology further bolsters accessibility and continuous monitoring capabilities. This study underscores the transformative impact of AI in respiratory health care, providing a scalable and economically viable option for asthma detection. Future initiatives aim to broaden the dataset and enhance model adaptability across varied patient demographics.