Twin transition, the simultaneous advancement of technology and circular practices, poses an under-operationalized challenge for industrial firms, particularly in balancing the pace of both dimensions. As a result, twin transition often remains a strategic aspiration rather than an actionable practice. To address this gap, we propose a route optimization model that operationalizes twin transition. Framed within a Socio-Techno-Ecological Systems perspective and developed using design science research, it mandates reduced CO₂ emissions as the default optimization criterion while allowing configurable parameters (i.e., distance, time, and cost). By integrating genetic algorithm with machine-learning techniques, the model enhances adaptive performance in real-time. Achieving 97.7% prediction accuracy with random-forest classifier, the model is validated on established datasets to ensure generalizability. By continuously processing real-time inputs on traffic, road conditions, environmental data, and regulatory constraints, it can improve route safety, efficiency, and compliance. This study aligns technological progress with environmental objectives and societal expectations and offers both theoretical and managerial contributions by helping industrial firms reduce CO₂ emissions, strengthen supply-chain resilience, and accelerate circularity implementation.