Conventional radiotherapy (CR) stands at a critical juncture, poised for transformation through the integration of cutting-edge technologies. This article explores the transformative potential of integrating generative artificial intelligence (GAI) and computational sustainability (CS) principles into CR. The convergence of GAI techniques, such as generative adversarial networks, with CS offers novel approaches for optimizing treatment planning, enhancing precision, and ensuring long-term sustainability in radiotherapy practices. We delve deeper into the personalized medicine strategy facilitated by generative models, taking into account patient-specific anatomical variations and dose optimization. The article highlights the role of GAI in adaptive radiotherapy, enabling real-time adjustments to treatment plans based on dynamic changes in patient anatomy. CS principles contribute to resource optimization and energy efficiency, addressing the environmental impact of CR practices. The synergy between GAI and CS fosters innovations in treatment techniques, data-driven decision-making, and ethical considerations, promoting equitable access and minimizing disparities. This article provides a comprehensive overview of the potential benefits and challenges associated with the integration of GAI and CS in CR, shaping the future of precision, efficiency, and sustainable radiotherapy practices.