Advanced analytical methods have gained importance in the present times to determine, predict, and shape buying behaviour of consumer decision-making in personal-use automobiles purchase. This review is a synthesis of the present-day information regarding the use of predictive analytics as an analytical instrument in consumer behaviour study in the automobile industry. The research area incorporates the methods of predictive modelling, consumer preference analysis, and sustainability-related decision drivers to offer a comprehensive view of automobile purchase behaviour.  Based on the study, the main result includes the fact that predictive analytics allows learning more about purchase intention, preference changes towards electric and environmentally friendly cars and inclusion of behavioural, social and environmental variables in forecasting. Besides, experiences of cross-industry applications, including food, fashion, and energy, prove that there are strategies that can be transferred, making predictive models of the automobile market more robust. The contribution of the review is that it integrates the disjointed research, balances theoretical models and their practical predictive uses, and identifies a research agenda that the integration of artificial intelligence, big data, and sustainability-driven analytics into the consumer behaviour of automobile consumers’ needs to take in the future. The contributions provide viable implications to manufacturers, policy makers, and marketers who seek to make their products development and marketing strategy to keep pace with the growing consumer expectations in sustainable mobility