The rapid evolution of direct-to-consumer (D2C) markets has intensified competition and increased the complexity of customer engagement, necessitating advanced growth frameworks beyond traditional marketing models. This study develops and empirically validates an Integrated AI-Driven Marketing Growth Model designed to enhance scalability and competitive performance in D2C landscapes. Using a multi-method quantitative framework combining structural equation modeling, machine learning algorithms, and cluster segmentation, the research examines the relationships among AI integration intensity, customer intelligence capability, personalization accuracy, campaign optimization efficiency, and revenue scalability outcomes. Results demonstrate that AI integration significantly influences revenue scalability both directly and indirectly through enhanced customer intelligence and personalization systems. Nonlinear predictive models, particularly artificial neural networks, outperform conventional approaches in forecasting customer lifetime value growth and scalability metrics. Cluster analysis further reveals distinct firm categories based on AI maturity levels, with advanced AI adopters exhibiting superior growth trajectories and competitive resilience. The findings highlight that scalable enterprise expansion in D2C markets requires systemic AI integration rather than isolated technological applications. By aligning predictive analytics, personalization engines, and strategic coordination mechanisms, organizations can achieve sustainable revenue growth, improved marketing ROI, and strengthened competitive positioning.