AI-based metamaterial design is transforming applications in optics, healthcare, acoustics, and power systems by enabling the development of novel materials with targeted control over electromagnetic, mechanical, and thermal properties. Traditional design methods are time-consuming and resource-intensive, while AI-based approaches leverage machine learning (ML) algorithms to automate and accelerate the design process, facilitating the discovery of new metamaterial structures with enhanced performance. AI can also improve data analysis and optimize design parameters that are difficult to achieve with traditional methods. The review discusses the transformative impact of AI in these fields, highlighting current challenges, emerging applications, and future directions. In acoustics, AI-based design has been used to develop acoustic absorbers and cloaks with improved performance. For example, ML-assisted subwavelength sound absorbers with coherently coupled weak resonances (CCWRs) have been designed to achieve broadband sound absorption. AI-based methods have also been used to design membrane-type acoustic metamaterials (MAMs) for insulation, with the Kriging surrogate model enabling the design of acoustic metamaterials with specific sound insulation frequencies and bandwidths. Additionally, AI has been used to design acoustic cloaks with multilayered core-shell configurations and to achieve omnidirectional acoustic cloaking against airborne sound. In optics, AI-based design has been used to develop multifunctional optic lenses and meta-grating structures. For example, AI-based methods have been used to design bifocal metalenses that can independently focus and its bidirectional circular polarized light. AI-based design has also been used to develop chiral metasurface multifocal lenses in the Terahertz band and miniaturized wide-angle fisheye lenses. Additionally, AI-based methods have been used to design high-performance optic metalenses with accelerated simulation. In healthcare, AI-based design has been used to develop diagnostic sensors and point-of-care devices. For example, AI-based methods have been used to design graphene-based metasurface refractive index biosensors for hemoglobin detection and hepta-band terahertz metamaterial absorbers for glucose detection. AI-based design has also been used to develop wearable sensors and tactile sensors for point-of-care applications. These AI-based metamaterials have the potential to improve the accuracy and sensitivity of diagnostic tools and enable real-time monitoring of patient health. The integration of AI with metamaterials is expected to lead to significant advancements in healthcare, including the development of more efficient and cost-effective diagnostic tools.AI-based metamaterial design is transforming applications in optics, healthcare, acoustics, and power systems by enabling the development of novel materials with targeted control over electromagnetic, mechanical, and thermal properties. Traditional design methods are time-consuming and resource-intensive, while AI-based approaches leverage machine learning (ML) algorithms to automate and accelerate the design process, facilitating the discovery of new metamaterial structures with enhanced performance. AI can also improve data analysis and optimize design parameters that are difficult to achieve with traditional methods. The review discusses the transformative impact of AI in these fields, highlighting current challenges, emerging applications, and future directions. In acoustics, AI-based design has been used to develop acoustic absorbers and cloaks with improved performance. For example, ML-assisted subwavelength sound absorbers with coherently coupled weak resonances (CCWRs) have been designed to achieve broadband sound absorption. AI-based methods have also been used to design membrane-type acoustic metamaterials (MAMs) for insulation, with the Kriging surrogate model enabling the design of acoustic metamaterials with specific sound insulation frequencies and bandwidths. Additionally, AI has been used to design acoustic cloaks with multilayered core-shell configurations and to achieve omnidirectional acoustic cloaking against airborne sound. In optics, AI-based design has been used to develop multifunctional optic lenses and meta-grating structures. For example, AI-based methods have been used to design bifocal metalenses that can independently focus and its bidirectional circular polarized light. AI-based design has also been used to develop chiral metasurface multifocal lenses in the Terahertz band and miniaturized wide-angle fisheye lenses. Additionally, AI-based methods have been used to design high-performance optic metalenses with accelerated simulation. In healthcare, AI-based design has been used to develop diagnostic sensors and point-of-care devices. For example, AI-based methods have been used to design graphene-based metasurface refractive index biosensors for hemoglobin detection and hepta-band terahertz metamaterial absorbers for glucose detection. AI-based design has also been used to develop wearable sensors and tactile sensors for point-of-care applications. These AI-based metamaterials have the potential to improve the accuracy and sensitivity of diagnostic tools and enable real-time monitoring of patient health. The integration of AI with metamaterials is expected to lead to significant advancements in healthcare, including the development of more efficient and cost-effective diagnostic tools.