In the face of intensifying environmental challenges and rapid digital transformation, startups are increasingly expected to pursue sustainable development while navigating severe resource constraints and the inherent liability of newness typical of emerging ventures. Although existing literature acknowledges the critical role of Green Knowledge Management (GKM) in driving corporate sustainability, significant theoretical gaps remain. Prior studies frequently conceptualize GKM as a homogeneous construct, thereby masking the complex operational realities of its underlying dimensions. Furthermore, while Artificial Intelligence (AI) is rapidly altering organizational learning, limited academic attention has explored how AI specifically acts as a dynamic catalyst to enhance green knowledge processes within the entrepreneurial context. To address these critical omissions, this conceptual paper develops a comprehensive framework that elucidates how AI-driven GKM processes positively influence Sustainable Performance (SP) in startups. Drawing upon the Resource-Based View, the Knowledge-Based View, and the Dynamic Capabilities View, the proposed framework models GKM as a multidimensional construct comprising five distinct dimensions: green knowledge acquisition, storage, sharing, application, and creation. The model posits that robust GKM routines function as the core operational infrastructure enabling startups to translate environmental awareness into sustainability outcomes, encompassing long-term economic viability, reduced ecological footprints, and enhanced social responsibility. Crucially, this paper conceptualizes AI not as a generic technological solution, but as an advanced dynamic capability. AI is theorized to positively moderate the framework by acting as a digital catalyst that strengthens the relationship between GKM and SP. By providing analytical velocity and reducing prediction costs, AI empowers startups to overcome human capital limitations, rapidly sense subtle ecological shifts, mitigate trial-and-error risks, and efficiently apply internal knowledge assets to changing market demands. Integrating sustainability, knowledge management, and digital capabilities, this research advances theoretical discourse by deconstructing GKM into specific operational routines and refining the conceptual understanding of AI’s moderating role. Ultimately, this conceptual study establishes a foundation for future empirical validation and offers actionable guidance for policymakers and founders to align knowledge routines with AI investments for enduring sustainable performance.