Recent advancements in artificial intelligence (AI) are reshaping core functions within healthcare and biopharmaceutical industries, particularly in diagnostics, personalized care, and drug development. However, the success of these innovations hinges on how well institutions manage their implementation. This systematic review investigates how innovation management influences AI adoption in healthcare and biopharma, highlighting both progress and persistent challenges. Following the preferred reporting items for systematic reviews and meta-analyses guidelines, this review was conducted using literature sourced from five major databases – PubMed, IEEE Xplore, Scopus, Web of Science, and Embase – focusing on peer-reviewed studies published between 2015 and 2024. A total of 82 studies were included, comprising 42 quantitative, 30 qualitative, and 10 mixed-methods studies. The population, intervention, comparison, and outcome framework guided study selection, while quality was assessed using the Joanna Briggs Institute checklist and Cochrane Risk of Bias 2.0 tool. Findings reveal that AI systems enable earlier disease detection, streamline patient triage, and improve operational workflows. In biopharma, companies, such as Moderna have shortened vaccine development timelines by integrating AI into molecular design. However, significant roadblocks remain, particularly regarding data privacy, infrastructure costs, and insufficient AI literacy among healthcare providers, especially in low- and middle-income countries. These barriers underscore the need for proactive innovation management approaches. To promote sustainable and ethical AI integration, this study recommends the development of governance frameworks, targeted workforce training, and increased interdisciplinary collaboration. As AI continues to evolve, managing its adoption thoughtfully will be essential to balancing technological potential with clinical realities and patient-centered care.