This scoping review aims to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in healthcare. The review focuses on assessing different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. The study identified 11 papers focused on optimizing model fairness in healthcare applications, highlighting the limited research in terms of disease variety and healthcare applications, as well as the accessibility of public datasets for ML fairness research. Pre-processing techniques were found to be the most commonly used approach for bias mitigation, with eight studies employing these methods. The review also discusses the need for broader data sources and more diverse applications to advance fair ML in healthcare, emphasizing the importance of collaboration with domain and legal experts to understand the specific context and ethical considerations. The paper concludes by providing useful reference material and insights for researchers, revealing the gaps in the field and highlighting the need for heightened research focus to cover diverse applications and various types of RWD.This scoping review aims to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in healthcare. The review focuses on assessing different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. The study identified 11 papers focused on optimizing model fairness in healthcare applications, highlighting the limited research in terms of disease variety and healthcare applications, as well as the accessibility of public datasets for ML fairness research. Pre-processing techniques were found to be the most commonly used approach for bias mitigation, with eight studies employing these methods. The review also discusses the need for broader data sources and more diverse applications to advance fair ML in healthcare, emphasizing the importance of collaboration with domain and legal experts to understand the specific context and ethical considerations. The paper concludes by providing useful reference material and insights for researchers, revealing the gaps in the field and highlighting the need for heightened research focus to cover diverse applications and various types of RWD.