Chronic diseases are a major global health challenge, characterized by their long term nature and complexity in diagnosis, prognosis, and treatment. Machine learning (ML) has emerged as a transformative tool in healthcare, offering significant advancements in early diagnosis, risk prediction, and personalized treatment planning. However, the application of ML models in healthcare is often hindered by their "black box" nature, raising concerns about their interpretability and trustworthiness in clinical settings. This review explores the integration of explainable artificial intelligence (XAI) techniques into chronic disease management, highlighting the importance of developing models that are both accurate and understandable. It discusses the challenges related to data integration from various sources, such as electronic health records (EHRs), genomics, and wearables, and examines the ethical, legal, and social implications of deploying AI in healthcare. Furthermore, the review investigates barriers to clinical adoption, including regulatory hurdles, clinician training, and workflow integration. Ultimately, the paper underscores the need for multidisciplinary collaboration and responsible innovation in order to ensure that AI models are ethically sound, clinically validated, and capable of improving patient outcomes in chronic disease care.