Sustainable bioprinting is a transformative approach in tissue engineering and regenerative medicine, offering solutions to environmental challenges while advancing functional outcomes. Yet, achieving true sustainability remains complex, requiring reductions in material waste and energy use, and scalable, resource-efficient fabrication without compromising biological performance. Artificial intelligence (AI) provides a powerful means to meet these demands through data-driven material design, predictive process optimization, and intelligent control systems that improve both efficiency and environmental impact across the bioprinting workflow. This review examines the integration of AI into sustainable bioprinting across four key areas: hydrogel material discovery and development, bioink screening, process parameter optimization, and AI-assisted intelligent printing. AI facilitates the design of eco-friendly hydrogels by predicting molecular interactions and tailoring structural properties. It also improves bioink formulation by optimizing printability, biocompatibility, and mechanical strength, thereby reducing reliance on resource-intensive trial-and-error experimentation. Furthermore, AI algorithms streamline workflows by dynamically adjusting printing parameters to improve fidelity and reduce waste, while advanced AI-assisted systems demonstrate the feasibility of multi-material, contactless bioprinting aligned with sustainability goals.