Machine learning (ML) and artificial intelligence (AI) have remarkable abilities to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of ML methods, viz., deep learning, and develop a general purpose AI tool - dPOLY - for analyzing molecular dynamics (MD) trajectories and predicting phases and phase transitions in polymers. An unsupervised deep neural network (DNN) is used within this framework to map a MD trajectory undergoing thermophysical treatment such as cooling, heating, drying, and compression to a lower dimension. A supervised DNN is subsequently developed based on the lower dimensional data to characterize the phases and phase transitions. As a proof of concept, we employ this framework to study the coil to globule phase transition of a model polymer system. We conduct coarse-grained MD simulations to collect MD trajectories of a single polymer chain over a wide range of temperatures and use the dPOLY framework to predict polymer phases. The dPOLY framework accurately predicts the critical temperatures for the coil to globule transition for a wide range of polymer sizes. This method is generic and can be extended to capture various other phase transitions and dynamical crossovers in polymers and other soft materials. It can also significantly accelerate polymer phase prediction and characterization. © 2021 American Chemical Society.