The challenge of predicting which individuals will relapse into addiction has long frustrated clinicians and patients alike, with current diagnostic tools offering little guidance for personalized treatment approaches. A new conceptual framework promises to change this landscape by integrating multiple biological and psychological markers into a unified predictive model. The MAC/MAB-RCS framework synthesizes four key domains: genetic and epigenetic biomarkers, stress-response systems, functional brain network patterns, and psychological traits associated with addictive behaviors. At its center lies the Regulatory Control State, a dynamic measure of an individual's capacity for cognitive control, emotional regulation, and motivational drive management. When this regulatory system becomes disrupted, the model predicts increased vulnerability to cravings, compulsive behaviors, and relapse regardless of the specific substance or addictive behavior involved. This represents a significant departure from traditional addiction models that focus primarily on substance-specific mechanisms. The framework's strength lies in its transdiagnostic approach, recognizing that underlying regulatory dysfunction may be a common pathway across different addictive disorders. For clinical practice, this could enable earlier identification of high-risk individuals and more targeted interventions based on specific regulatory deficits rather than one-size-fits-all treatment approaches. However, the model remains conceptual at this stage, requiring extensive validation studies to demonstrate its predictive accuracy and clinical utility across diverse populations and addiction types.