Mixed-Methods Research (MMR) integrates quantitative and qualitative approaches to provide a comprehensive understanding of complex research questions. This methodology is increasingly applied across disciplines such as healthcare, education, and business, due to its capacity to capture both statistical patterns and contextual insights. MMR allows for the integration of diverse data types, offering a more nuanced and in-depth analysis of phenomena. The chapter explores different MMR designs, including convergent, explanatory, exploratory, and embedded methods, each suited to address specific research objectives. The chapter also addresses the growing role of generative AI in MMR, highlighting its potential to automate data analysis, facilitate data integration, and enhance the depth of interpretation. However, challenges such as ethical concerns, the need for human oversight, and the complexity of data integration persist. Despite these challenges, MMR remains a valuable methodological framework, offering a robust means of combining the strengths of qualitative and quantitative research to address multifaceted research problems.