Multi-frequency fringe projection profilometry (MF-FPP) is a key technique for three-dimensional (3D) surface measurement, valued for its precision and applications in manufacturing, biomedical imaging, and dynamic scene analysis. Despite significant advances, MF-FPP remains constrained by challenges in phase unwrapping, noise robustness, measurement efficiency, and frequency optimization. This study presents a literature review of 30 peer-reviewed studies (2006–2024), selected through database searches and citation chaining, focusing on phase unwrapping strategies. Analysis was organized around phase accuracy, measurement efficiency, noise suppression, frequency selection, and integration of deep learning with classical methods. Findings reveal that adaptive algorithms and deep learning have substantially improved phase retrieval and enabled single-shot 3D reconstruction, enhancing real-time applicability. Noise robustness has benefited from both algorithmic corrections and neural networks, though speed–resilience trade-offs persist. Frequency optimization shows potential for improving sensitivity but remains underdeveloped and seldom integrated with AI-based approaches. The review identifies gaps, including limited generalization of deep learning models, underexplored hybrid frameworks, and sparse real-world validation. Its novelty lies in synthesizing algorithmic and data-driven strategies, demonstrating how hybrid approaches can overcome methodological limitations. By consolidating fragmented findings and highlighting emerging directions, such as adaptive frequency optimization and self-supervised learning, this study provides actionable insights for advancing MF-FPP. The implications extend across industrial quality assurance, healthcare diagnostics, infrastructure monitoring, and cultural heritage preservation