The spread of infectious diseases has become a significant global public health concern, necessitating effective tracking and prediction methods. The COVID-19 pandemic has highlighted the critical role of data analysis in forecasting disease outbreaks and assessing vaccine effectiveness. This study explores the application of statistical modeling, machine learning to predict the future course of a pandemic and evaluate the impact of vaccination programs. By analyzing key factors such as infection rates, vaccination coverage, emerging variants, and public health policies that can inform decision-making and improve pandemic preparedness. Time-series forecasting models such as Moving Average method is utilized to predict future infection trends, while machine learning technique such as linear regression help assess vaccine efficacy by analyzing hospitalization rates, mortality reduction, and immunity duration. The findings of this study highlight the effectiveness of vaccines in reducing severe cases and controlling the spread of infectious diseases. This research underscores the importance of data-driven approaches in guiding public health policies and ensuring a proactive response to future pandemics. Here the covid19 dataset which is taken as input include all types of emerging variant which has occurred during the pandemic situation.