Deep Reinforcement learning is incorporated in trajectory data clustering to investigate the trajectories gathered from medical information’s. Generally Trajectory mining determines the patterns in data, detects anomalies, and does informative clustering, location prediction, and classification. The main intent of Medical trajectory data clustering is identifying the trajectories with identical patterns for better patient treatment outcomes. Medical trajectory data stored in a multidimensional format which is further processed using the machine learning and deep learning architectures. Machine learning approaches employed to mine trajectory data and identifying the future treatment is a complicated task. To deal with this, the deep learning approaches in trajectory mining concentrate to eliminate the computational complexity on type 2 diabetic’s data. To overcome this problem, deep reinforcement learning for medical trajectory data clustering approach is proposed that is a combination of various strategies to flexible adapt to changes of the trajectory data. After the proposed pre-processing and feature transformation, features are clustered on basis of the weights of the model with lesser efforts and the proposed clustering plays a key role in the process of multi-attribute trajectory data investigation. The proposed deep learning methodology is more suitable for clustering the multi-attribute trajectory with fewer complexity computations than existing machine learning based methods. The experimental results also states that the results of deep reinforcement learning are promising than the other approaches with respect to precision, Recall and F Measure respectively.