The convergence of artificial intelligence (AI) and telehealth is reshaping the landscape of modern healthcare, offering scalable, efficient, and cost-effective solutions to address persistent disparities in access and quality of care. In many regions, underserved populations face significant barriers such as geographic isolation, limited healthcare infrastructure, and prohibitive costs, leading to unequal health outcomes. This study investigates how AI-driven telehealth solutions can bridge these gaps, focusing on innovative business models and strategic approaches that enhance healthcare accessibility, affordability, and equity. By automating diagnostics, optimizing treatment pathways, and enabling real-time patient monitoring, AI technologies can extend specialized care to remote communities and reduce the strain on overburdened healthcare systems. The research delves into sustainable business models, including subscription-based services, outcome-driven partnerships, and hybrid public-private collaborations that align economic incentives with improved health outcomes. These models not only facilitate the scalability of telehealth solutions but also ensure affordability for patients and sustainability for providers. Moreover, the study explores the ethical, regulatory, and technical challenges inherent in deploying AI in telehealth, such as data privacy, algorithmic bias, and the need for transparent, explainable AI systems to foster patient and clinician trust. Through a comprehensive analysis of case studies and emerging industry trends, this study highlights the potential of AI-driven telehealth as a pivotal tool in national health policy. It provides a roadmap for leveraging technology to reduce healthcare disparities, promote health equity, and create more resilient, patient-centered healthcare systems globally.