Regenerative design lies on synergistic relationship between sociocultural and ecological systems, which can enable revolutionary boundaries for designing decision-making frameworks. Transitioning to regenerative design as a manifestation of systems thinking necessitates a fundamental shift from sustainable patterns and mechanistic design methodologies. At its core, regenerative design unlocks a holistic paradigm that fosters circular systems reliant on renewable resources, which can strive for equilibrium between creation and utilization. This framework goes beyond mere sustainability by actively engaging in the restoration and regeneration of its sources of energy and materials. It aspires to harness the inherent wisdom of nature, facilitating a comprehensive harmonious coexistence with environment. The integration of data-driven decision-making and regenerative paradigms can provide an insight for developing evidence-based solutions for strategic environmental and natural resource management through design practices. This short research presents a holistic data-driven and self-adaptive design strategy as the integrated problem-solver model under the imperatives of regenerative adaptive design and transfer knowledge system capable of the extensive range of applications from microscale to macroscale. The underlying idea proposes orientation on machine learning feedback loop mechanisms and nested coevolutionary loops embedded in an inclusive feedback loop frame, synergistically interfaced with the typologies of monitoring systems and intuitive datasets to problem-solve at the intersection of design, construction, and built environment. This design model can support designers, planners, and city managers in optimizing their decision-making process by relying on precise data-driven feedback in different scales of complex systems, from living bits to ecological living environments.