As global economies tried to cope up with the severe negative effects of the Covid-19 pandemic, the advent of preventive vaccine doses brought rays of hope across the globe. Even though most people accepted the vaccines, many had doubts about the vaccines’ efficacy and potential drawbacks. The current study attempts to identify factors that influence the public intention to take vaccine shots. A survey conducted by selecting respondents via random sampling from different parts of India received 250 valid responses. Exploratory Factor Analysis (EFA) reveals that “scepticism about vaccines”, “concern about effectiveness of vaccines”, “positivity towards vaccines”, “beliefs of people” and “attitude towards preventive measures” are some of the key factors that may influence the adoption of Covid vaccines. The dependent variable in the study (whether people have taken booster dose or not) is nominal and binary in nature. State of the art classical (Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression and Decision Tree- C5.0) and ensemble machine learning algorithms (Adaboost, Bagging) have been implemented on the collected data under different training-testing partitions of the dataset- 80-20, 60-40 and 50-50. After using oversampling to solve the problem of class imbalance, Logistic Regression returns the best results- overall accuracy-81%, sensitivity-83% and specificity-80.5%. Further, “the presence of comorbidities”, “precautionary measures” and “family member hospitalization” are the other significant factors that influence vaccine acceptance. This is one of the few studies to explore the public intention to take Covid vaccines using machine learning algorithms.