Autism Spectrum Disorder (ASD) poses a notable developmental obstacle linked to neural abnormalities, characterized by repetitive and non-functional behaviour. The sensory overloads lead to elevated aggression, social phobia, tantrums, hypervigilance, and heightened sensitivity to external stimuli, necessitating heightened awareness. Caregivers encounter challenges in ensuring constant monitoring and surveillance. In response to these difficulties, a project has been devised, integrating diverse sensors on a wearable device to identify the environmental disturbances through PGP for heart rate, MEMS for stereotypic sensing,GSR to analyse the sweat, also external stimuli as for over loudness and suspicious gas, an IoT module with Raspberry pi, and machine learning methodologies for classifying and categorisation exact situation by analysing data using Convolution Neural Network CNN with better 97.6% accuracy. This helps to give immediate alert to caregivers by GSM module also with self-assistance. The central control of the project is facilitated by a PIC microcontroller and driving relays. Through the monitoring of IoT-collected data, a detailed analysis illustrates the frequency of incidents, including aggressiveness detected by the accelerometer sensor, heightened gas levels, along with increased occurrences of loud noise noted along with the heartbeat and sweat sensor. This analytical method empowers caregivers and doctors with a comprehensive understanding of the patient’s medical condition.