Abstract—Appointment booking is a part of everyday life nowadays. From booking movie show tickets to booking flight seats everything is online. In recent times the pandemic has brought some tough challenges before the healthcare systems which include appointment booking. As hospitals and clinics witness an overwhelming surge of patients the healthcare workers are found to be overburdened. Oftentimes important calls made by patients to the hospitals go unanswered or result in long wait times. Speech is the primary mode of communication among human beings. Many patients would prefer to call-in and book an appointment before visiting the crowded hospitals. This paper is focused on developing an AI voice bot which works on call to communicate with patients and subsequently book appointments and/or answer questions related to that hospital. Automatic Speech Recognition (ASR) is the process of deriving the transcription of an utterance, given in the speech waveform. This paper aims at completely automating the process of booking appointments in general, all through voice recognition. Instead of having to go through contact lists and learn different UIs, patients can simply have a conversation with the bot through a familiar interface. Not only will it book an appointment but also study the patterns in the database and predict future inflow of patients and suggest constructive advice for the system. This will not only save patient’s critical time, but also reduce the burden on healthcare workers and hence optimize the management of patients and their appointments. In this repository I've added data science Python files which processes the Realtime and Test datasets. Test dataset has been taken from Kaggle whereas Realtime dataset has been generated with the help of prospective patients. Exploratory Data Analysis has been performed along with various different visualizations in the form of histograms, scatterplots, barplots, heatmaps etc. Apart from these a Comparison python notebook has been added which weighs out the differences between the two datasets and gives the accuracy of the model.