Malaria, a dangerous parasitic disease spread by mosquitoes, remains a major health concern in many parts of the world, including India. To effectively combat malaria, it's crucial to understand the factors that influence its spread. This study focuses on Chengalpattu District in Tamil Nadu, India, aiming to unravel the complex relationship between weather, mosquito behavior, and malaria transmission. By combining predictive modeling with existing knowledge about disease outbreaks, the research provides valuable insights for developing effective prevention and control strategies in the region. We employ both traditional predictive models (Decision Tree, K-Nearest Neighbors, Random Forest) and a modified SEIR (Susceptible-Exposed-Infectious-Recovered) model to explore these relationships. The predictive models are evaluated for their accuracy in predicting mosquito type and species based on climatic variables, age, gender, and malaria case data. Further analysis utilizes the SEIR framework, alongside SIR epidemiological models, to gain deeper insights into malaria transmission dynamics by analyzing the susceptible, exposed, infectious, and recovered populations. Comparative analyses between these models and the traditional predictive approaches emphasize the crucial role of integrating weather parameters, mosquito behavior, and seasonality in understanding malaria transmission patterns. Major findings reveal a significant correlation between weather conditions, particularly rainfall and temperature, and malaria transmission, with peak transmission periods observed between July and December. These insights underscore the necessity for targeted interventions, including vector control measures and enhanced surveillance, specifically during thesepeak transmission periods. This comprehensive analysis contributes to our understanding of malaria transmission dynamics in Chengalpattu District and informs evidence-based strategies for malaria control and prevention in endemic regions.