Unconsciousness is a general phenomenon during epilepsy seizures which might lead to serious accidents and even death if help is not provided on time. Epileptic patients cannot work independently as there is always a fear of seizure which might also endanger the people surrounding them, leading to social isolation of the patient. We propose an easy-to-use wearable band to monitor the body’s vital statistics \(24\times 7\) , viz. heart rate, oxygen level, temperature, muscle spasms, and efficiently correlate these vital stats with a normal profile recorded for the individual. The idea is to have a cost-effective personalized prediction regime for the individual by collecting data sequentially and deploy a multivariate long short-term memory autoencoder model on AWS SageMaker with the data from the sensors and subsequently improving the algorithm’s performance with the incoming data in real time. Once a seizure is predicted, the device alerts a designated caretaker about an upcoming seizure and will also call the emergency service in case of “Amber Alert” stage. This occurs when seizures last longer than 3 mins and epilepsy could be life-threatening without immediate medical assistance. The best feature is that the band can even work in offline mode after training phase of band is over. The paper depicts the working of the band and its integration with our mobile application and cloud services like Amazon SageMaker for deploying LSTM autoencoder model.