The article reviews the development and applications of digital holography (DH) in quantitative phase imaging. DH, introduced by Dennis Gabor in 1948, has evolved over 75 years to become a powerful tool for measuring optical wavefronts and phase information. The technology is particularly useful for non-contact, precise, and dynamic measurements of complex waves, making it valuable in physics, biology, and materials science. DH can visualize refractive index and thickness distributions in weakly absorbing samples, which is crucial for understanding various diseases and materials. The article discusses the fundamental principles of DH, including the transformation between the complex domain and the real domain, and the mathematical and physical principles of reconstruction. It reviews the historical development of DH, from its early stages to the integration of computational resources, which has led to significant advancements in techniques such as phase retrieval, holographic multiplexing, and deep learning. The article also explores the multidisciplinary applications of DH, including cell biology, analytical chemistry, clinical sciences, and medical imaging. Finally, it highlights emerging challenges and opportunities, such as combining DH with other methodologies to expand its scope and utility.The article reviews the development and applications of digital holography (DH) in quantitative phase imaging. DH, introduced by Dennis Gabor in 1948, has evolved over 75 years to become a powerful tool for measuring optical wavefronts and phase information. The technology is particularly useful for non-contact, precise, and dynamic measurements of complex waves, making it valuable in physics, biology, and materials science. DH can visualize refractive index and thickness distributions in weakly absorbing samples, which is crucial for understanding various diseases and materials. The article discusses the fundamental principles of DH, including the transformation between the complex domain and the real domain, and the mathematical and physical principles of reconstruction. It reviews the historical development of DH, from its early stages to the integration of computational resources, which has led to significant advancements in techniques such as phase retrieval, holographic multiplexing, and deep learning. The article also explores the multidisciplinary applications of DH, including cell biology, analytical chemistry, clinical sciences, and medical imaging. Finally, it highlights emerging challenges and opportunities, such as combining DH with other methodologies to expand its scope and utility.