Creation of synthetic data in healthcare research has emerged as a compelling solution to address challenges related to data-sharing privacy and scarceness of health data for training Machine learning models. Unlike technical discussions, this review delves into the fundamental factors that govern the circumstances under which synthetic data is beneficial or contraindicated for addressing clinical research questions. By examining the landscape of synthetic data adoption in healthcare, we highlight the methodological and utility considerations that shape its integration into the research ecosystem. We elucidate how synthetic data can expedite research initiatives, support data-driven decision-making, and facilitate innovative methodologies while safeguarding sensitive patient information. Conversely, we explore the potential pitfalls of using synthetic data in a real-world case involving lung cancer patient data. We explore avenues for refining synthetic data generation techniques, enhancing data quality, and strengthening the framework for ethical implementation.