This study presents an advanced quantitative framework for modelling AI-generated consumer behaviour within the rapidly evolving digital economy. As artificial intelligence (AI) increasingly shapes personalized recommendations, pricing strategies, and online engagement, understanding consumer responses to algorithm-driven platforms has become crucial for both businesses and policymakers. The proposed model integrates behavioural economics with machine learning to capture the dynamic patterns of consumer decision-making under algorithmic influence. Specifically, the framework employs an Agent-Based Modelling (ABM) approach combined with Reinforcement Learning (RL) techniques to simulate how consumers adapt their preferences and purchasing choices in response to real-time stimuli such as targeted advertisements, dynamic pricing, and social network effects. This hybrid model not only provides micro-level insights into individual behavioural adaptations but also connects them to macro-level market dynamics. The results demonstrate the framework’s potential in predicting emerging consumption trends, guiding data-driven marketing strategies, and informing regulatory policies in AI-powered digital ecosystems.