AI-energized hydrogel design, optimization, and application in biomedicine have emerged as a revolutionary breakthrough in materials science. Traditional hydrogel design and optimization methods are time-consuming and expensive, leading to slow development. AI technology, combined with increasing material data, has enabled efficient design and optimization of hydrogels for biomedical applications. This review outlines the history of AI, its advantages in hydrogel design, and its applications in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors. It also discusses future directions and prospects for AI-energized hydrogels. Hydrogels, with their high water content and similarity to natural tissues, have broad applications in drug delivery, wound dressing, contact lenses, bone tissue engineering, and biosensors. However, their development is hindered by challenges such as complex design processes, time-consuming optimization, and long application cycles. AI offers solutions by enabling predictive modeling, multi-attribute optimization, high-throughput screening, and automated material discovery. AI can predict and optimize hydrogel composition and properties, improve drug delivery systems, and enhance the performance of hydrogels in biomedical applications. AI-energized hydrogels have potential applications in drug delivery systems, bio-inks for 3D printing, tissue repair, and biosensors. AI can assist in designing and manufacturing these hydrogels, improving efficiency and safety. In drug delivery, AI can predict hydrogel formation, optimize hydrogel performance, and tune drug release profiles. In 3D printing, AI can enhance printability and facilitate the development of bio-inks for personalized medical applications. In tissue repair, AI can analyze wounds, select appropriate hydrogel materials, and optimize hydrogel performance for effective healing. AI-energized hydrogels offer significant advantages in biomedical applications, including improved material design, optimization, and performance. They enable efficient characterization, high-throughput screening, and predictive modeling, accelerating the development of hydrogels for biomedical use. The integration of AI with hydrogels has the potential to revolutionize biomedical applications, providing new perspectives for research and development in this field.AI-energized hydrogel design, optimization, and application in biomedicine have emerged as a revolutionary breakthrough in materials science. Traditional hydrogel design and optimization methods are time-consuming and expensive, leading to slow development. AI technology, combined with increasing material data, has enabled efficient design and optimization of hydrogels for biomedical applications. This review outlines the history of AI, its advantages in hydrogel design, and its applications in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors. It also discusses future directions and prospects for AI-energized hydrogels. Hydrogels, with their high water content and similarity to natural tissues, have broad applications in drug delivery, wound dressing, contact lenses, bone tissue engineering, and biosensors. However, their development is hindered by challenges such as complex design processes, time-consuming optimization, and long application cycles. AI offers solutions by enabling predictive modeling, multi-attribute optimization, high-throughput screening, and automated material discovery. AI can predict and optimize hydrogel composition and properties, improve drug delivery systems, and enhance the performance of hydrogels in biomedical applications. AI-energized hydrogels have potential applications in drug delivery systems, bio-inks for 3D printing, tissue repair, and biosensors. AI can assist in designing and manufacturing these hydrogels, improving efficiency and safety. In drug delivery, AI can predict hydrogel formation, optimize hydrogel performance, and tune drug release profiles. In 3D printing, AI can enhance printability and facilitate the development of bio-inks for personalized medical applications. In tissue repair, AI can analyze wounds, select appropriate hydrogel materials, and optimize hydrogel performance for effective healing. AI-energized hydrogels offer significant advantages in biomedical applications, including improved material design, optimization, and performance. They enable efficient characterization, high-throughput screening, and predictive modeling, accelerating the development of hydrogels for biomedical use. The integration of AI with hydrogels has the potential to revolutionize biomedical applications, providing new perspectives for research and development in this field.