The electric vehicle (EV) industry is undergoing unprecedented transformation globally, and particularly in India where the government has set ambitious targets of 30% EV sales in private cars and 80% in two-wheelers by 2030. However, the transition faces significant hurdles, including a persistent "attitude-action" gap where consumers express positive sentiments toward EVs but fail to convert those attitudes into actual purchases. This research addresses the critical gap in literature by applying predictive modelling techniques to identify potential EV buyers using demographic and behavioural data specific to the Indian market. The study employed a descriptive research design with a sample of 150 respondents, utilizing a structured questionnaire. Data analysis was conducted using simple percentage analysis, chi-square tests, correlation, ANOVA, and the Random Forest algorithm. The findings reveal that the majority of potential EV buyers are male (54.7%), aged 21-30 years (40%), with diploma qualifications (36.7%), working as businessmen (33.3%), and earning between Rs. 25,001-30,000 per month (39.3%). The Random Forest model achieved a predictive accuracy of 76.7% with zero false negatives. Behavioural factors including regular monitoring, validation practices, and planning were found to be stronger predictors than demographic variables alone. Risk management was identified as the most significant benefit of predictive modelling, with 47.3% agreement. The study concludes that predictive modelling is a strategic necessity for EV businesses and policymakers navigating India's evolving electric vehicle market.