<p>Multimorbidity, the coexistence of multiple chronic health conditions within an individual, significantly impacts quality of life, reduces life expectancy, and increases the burden on healthcare systems. With its prevalence rising due to aging populations, managing multimorbidity requires continuous, integrated care, challenging patients and healthcare providers. This perspective explores the potential of Artificial Intelligence (AI) in improving the management of multimorbidity by optimizing treatment strategies, enabling early risk identification, and enhancing healthcare resource allocation. The application of AI in healthcare is reviewed, focusing on advanced algorithms, data-driven decision-making, and predictive analytics. Emphasis is placed on how AI analyzes vast patient datasets to provide personalized care and identify high-risk individuals for timely intervention. AI-driven tools demonstrate the ability to tailor treatment strategies to individual patient needs, improve care quality, and facilitate proactive measures to prevent adverse outcomes. Predictive analytics enhance the identification of high-risk patients, while resource optimization benefits healthcare systems managing multimorbidity. AI offers a transformative approach to managing multimorbidity by personalizing care, enabling early interventions, and optimizing healthcare resources, thus improving patient outcomes and easing systemic burdens. These findings underscore the potential of integrating AI into multimorbidity care frameworks.</p>