Learning continually, to accumulate knowledge, is one of the prominent features of human intelligence. Unlike this, deep learning-based AI models are mostly static under a strong assumption that all the distributions are known apriori and are pre-defined in the training data. However, the real-world is uncertain, volatile, and full of unknowns. Although there are independent bodies of research in detection of unknowns and representational adaptations, there is a huge gap in models that are capable of seamlessly detecting and adapting their representations with unknowns. This proposal aims at developing Self-Aware Continuous Learning Models (SACoLM) that are continuously aware of their representations, detect novel distributions that are unknown to them, estimate the complexity of the unknown distributions, choose relevant data to represent the unknown distributions and adapt themselves to represent these previously unknown distributions without catastrophically forgetting any previously learnt distribution. The project will build AI models that learn throughout life, to enable critical applications that warrant dynamic and up-to-date decision making, in diverse application domains.