Introduction: Japanese encephalitis (JE) remains a major public health concern in India, particularly in rural and peri-urban regions where environmental conditions favor vector proliferation. The primary vectors, especially Culex species, are highly sensitive to climatic and land-use changes. Predicting future vector distribution under changing environmental scenarios is critical for proactive surveillance and disease prevention. This study proposes the use of a species distribution modeling approach to map the current and future distribution of Japanese encephalitis vectors in India and to estimate potential vector abundance and associated disease risk by 2030. Methods: Occurance records of confirmed JE mosquito vectors, including Culex tritaeniorhynchus, will be compiled from entomological surveys, literature reviews, and published databases across India. Environmental predictors such as temperature, precipitation, vegetation indices, elevation, and land-use variables will be incorporated. Species distribution models will be developed using MaxEnt, which applies a maximum entropy algorithm to estimate habitat suitability from presence-only data. Future projections for 2030 will be generated using climate scenarios derived from global climate models (GCMs). Habitat suitability outputs will be integrated with human population density and historical case data to model potential changes in vector abundance and projected JE risk. Results: The model is expected to indentify high-suitability zones in endemic states and predict geographic expansion or shifts in vector distribution by 2030. Areas with increasing climatic suitability may correspond to higher projected vector abundance and elevated JE transmission risk. Conclusion: MaxEnt-based predictive modeling offers a robust tool for forecasting JE vector distribution and future disease risk. Integrating ecological suitability with epidemiological data can support targeted vector control, vaccination planning, and early warning systems for Japanese encephalitis in India.