Children with autism often experience sudden meltdowns which not only makes the moment tough for the caretakers/parents but also make the children hurt themselves physically. Studies have discovered that children with autistic spectrum disorder exhibit certain actions through which we can anticipate mutilating meltdowns in them. The objective of the project is to build a system that can recognize such kind of actions using deep learning techniques thereby, notifying the caretakers/parents so that they can get the situation under control in lesser time. Using deep learning RCNNs, researchers can train the system faster yet reliable because unlike all the machine learning algorithms, deep learning algorithms are more efficient and have more scope into future. They have trained a classifier on images that are gathered from videos and reliable internet sources with most predictive gestures, through which they can detect the meltdowns more precisely. Researchers have trained a model that validated the accuracy by ~93% which is accompanied by a loss/train classifier with a minimal 0.4% loss. Functional testing was done through feeding the deep neural network with chosen actions performed by five individuals that resulted in an accuracy of ~92% in all cases, which can assure the real-time usage of the system.