Introduction: Corona has affected everyone__ampersandsignrsquo;s lives. Most companies and services have found new ways to approach their work while at the same time preventing the further spread of the virus. The mode of travel has also significantly changed. Governments are trying their best to maintain all possible safety norms at airports and railway stations. The idea is to ensure that people are maintaining social distancing and wearing a mask. Objectives: The main aim of the proposed work (AI-CAPM) is to reduce the contact between staff and the passengers. To ensure the genuineness of the Aadhar card and other IDs, the system uses face detection algorithms that will detect the faces and only allow those faces who have their tickets booked for that particular day. Methods: The proposed work uses Local Binary Patterns Histograms (LBPH) Face Recognizer for face recognition, Social Distancing Monitor Using DNN and Mask Detection with CNN. Results: This research work builds a social distancing monitor model using Single Shot Detector (SSD). SSD takes one single shot to detect multiple objects within the image. SSD covers all computation in a single network by eliminating subsequent pixel or feature resampling stages and object proposal generation. This makes SSD easy to train and simple to integrate into systems that require detection component. SSD is faster and has much better accuracy compared to other algorithms. Conclusion: The software developed in this work proposes an autonomous verification of a passenger as it displays the passenger__ampersandsign#39;s__ampersandsignnbsp;train/flight details and boarding area by recognizing them from the passenger__ampersandsignrsquo;s database. This removes the need for manual checking of the passenger before boarding. Overall, the software developed automates tasks and reduces human involvement as it is the need in this pandemic struck world to contain the spread of the virus.