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The intensive and explosive behavioral problems associated with Autism Spectrum Disorder (ASD) are treated as ‘meltdown or tantrum,’ and it may lead to hyperactivity, impulsivity, aggression, self-injury, and irritability. The present work aims to propose and implement a noninvasive real-time deep learning based Meltdown/Tantrum Detection System (MTDS) for ASD individuals. The noninvasive physiological signals (such that heart rate, skin temperature, and galvanic skin response) were synthetically recorded with a specially designed hardware prototype. The recorded physiological signals were transmitted to an internet connected server where deep learning algorithms such as CNN, LSTM, and CNN-LSTM based Meltdown/Tantrum Detection System (MTDS) were implemented. The trained deep learning model was capable of detecting abnormal states of meltdown or tantrum through real-time received physiological signals. The proposed MTDS system was trained and tested with deep learning algorithms such as CNN, LSTM and hybrid CNN-LSTM, and it was found that hybrid CNN-LSTM was outperformed with an average training and testing accuracy of 96% with low MAE (0.10 for training and 0.04 for testing). Furthermore, 86% of the ASD caregivers favored the proposed MTDS system.