With the transformative role of Artificial Intelligence in chronic disease management, telemedicine and AI-driven tools enable early diagnosis, personalized care, and continuous monitoring. In the Healthcare 5.0 framework, sustainable telehealth systems enhanced by AI facilitate real-time data analysis and predictive insights, advancing clinical outcomes and optimizing resource use. The paper presents a thorough examination and analysis of AI-assisted telemedicine interventions concerning chronic disease management through case studies: Propeller Health for asthma, MySugr for diabetes, PathAI in pathology insights, DeepMind Health for predictive health diagnostics, and BenevolentAI for oncology, emphasizing their approaches, efficiencies, and outcomes. This study comparatively analyzes the above case studies against notable advances they made in patient care, including enhanced diagnostic accuracy and personalized treatment plans. Key contributions include a comprehensive evaluation of supervised and unsupervised machine-learning models to predict the disease, evaluations of telehealth solutions for AI-enabled remote patient monitoring, and examples of how machine learning algorithms have enhanced diagnostic accuracy and individualized treatment plans. Our systematic approach from the methodology on study selection and evaluation itself assures the reliability and relevance of the findings. Findings suggest that these intelligent technologies hold significant promise in chronic disease management but run into real issues of data privacy, scalability of models, and adaptability in varied healthcare settings. Through discussions on current outcomes, implications, limitations, and future directions, aims to provide a strategic framework for incorporating AI in telemedicine, encouraging the development of flexible, secure, and ethically driven AI models for healthcare deployment. This research ultimately contributes to the scientific discipline, strengthening that vision of adaptively advanced AI models that will enhance access to high-quality healthcare and raise efficiency, ultimately paving the way for sustainable data-driven healthcare interventions. By exploring the potential of AI to significantly improve patient outcomes and reduce healthcare costs, this paper advocates for innovations that address accessibility challenges in chronic disease management. Ultimately, the findings point out the possibility for AI and telemedicine to have a greater role in access to individualized care, thus helping close existing care gaps while optimizing chronic care management solutions within a patient-centered context.