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This paper reviews the growing role of data-driven decision making (DDDM) in modern curriculum design. It explores how educational data—such as academic performance, attendance records, engagement metrics, and feedback—can be effectively used to improve teaching and learning outcomes. The study discusses various tools and techniques, including machine learning models, data visualization platforms, and learning analytics, that help educators make informed curricular decisions. It also highlights key frameworks such as Outcome-Based Education (OBE) and Bloom's Taxonomy, emphasizing the importance of ethical data use, privacy, and institutional readiness. The paper concludes by identifying future research opportunities in AI-assisted curriculum co-design and adaptive learning systems.