Infectious disease forecasting, nowadays, is of great importance in the management of the public health of the country, India, because of its wide multiple populations and varied environmental conditions, which are excellent challenges. In this paper, a clustering-based multivariate prediction model is presented that can accurately forecast new infectious disease cases through capturing interactions between disease spread and sources of data: environmental factors, demographic information, and history of disease trends. Such an approach would involve the collection of extensive data from multiple valid sources such that variables include temperature, humidity, and population density. Clustering techniques would then sort the data points into different clusters to develop characteristic epidemiological profiles that permit corresponding forecasts. With this segmentation, the model would be able to understand regional differences in susceptibility to disease-making more context-specific forecasts. Advanced multivariate prediction techniques, such as regression analysis and machine learning algorithms, are employed to explain the relationship between identified clusters and the incidence of disease. The same model is accessed by the historical data as well and has been found to make a significant difference in the accuracy of forecasting by improving over the traditional ways of doing so. This may help intervene in public health well in time that can reduce the burden on the healthcare system. This model provides insights that have been helpful for public health officials in identifying high-risk areas with particular strategies. Based on these observations, policymakers can decide on resource allocations and preparations for outbreaks. The method proposed here is a robust framework to raise predictability for infectious diseases in India by multivariate clustering-based predictive models. That is, it not only improves predictive accuracy but also helps in proactive public health strategies to bring better management of infectious disease threats in an environment that is rapidly changing. Future research will thus aim at focusing on perfecting the model coupled with real-time data to make the effectiveness of the same even better.