Over the years, COVID-19 has engaged the world in vaccine development and utilization, including mRNA vaccines such as Moderna’s mRNA-1273. However, for a practice to endure and remain effective, the support has to be backed by sound data analytic approaches. This paper presents a large-scale, realworld study for monitoring and assessing the efficacy of the mRNA-1273 vaccine by utilizing public health and EHR data until December 2021. Thus, we additionally used data mining, statistical modelling, and machine learning techniques on the sample of more than one million people of different ages, sexes, and occupations. We established sex differences in vaccine finesses, the protective intervals in different ages, with and without comorbidities, and geographical locations and decreased efficacy against different strains, including the Delta strain. Moreover, we support a real-time effectiveness prediction technique based on logistic regression random forest. They, therefore, call for active booster campaigns and specific health policy recommendations. The paper also underscores the importance of uniformity in data gathering and the cooperation between the agencies. By further emphasizing understanding the prospect of future pandemics and one-vaccine tracking, this essay influences the subject of proactive planning for pandemics and real-vaccine surveillance systems for the better.