The development of computational methods specifically designed for mental health and well-being is critical to addressing global societal challenges, as these technologies have the potential to support prevention, assessment, and intervention at scale, while ensuring an ethical, responsible, and transformative impact on individual and collective outcomes. These methods could provide insights into the understanding, diagnosis, and monitoring of mental health conditions by delivering personalized and cost-effective solutions to improve health outcomes and quality of care, while enabling early intervention, real-time support, and long-term well-being. They could analyze and integrate data from brain activity, behavior, and symptom patterns to elucidate underlying mechanisms of psychological responses, as well as potential neuropsychiatric pathways and trajectories of care for mental disorders. However, the adoption of computational models in this specific context raises several challenges that must be addressed, ranging from the need for high-quality datasets, which require continuous interplay among researchers from different disciplines, to the design of systems that protect user privacy, ensure equity, and prevent algorithmic bias in sensitive mental health applications, to name a few. In this context, the Special Session on “Computational Methods for Mental Health and Well-Being” aims to foster cross-disciplinary exchange among computer science, psychology, mental healthcare, and ethics. It reflects the growing importance of technology in improving mental well-being and seeks to stimulate collaborative discussions focused on integrating computational methods into healthcare systems, identifying gaps in research and applications, and critically examining the opportunities and risks associated with the adoption of technologies for managing highly sensitive data.