Today, there is a current and growing crisis in power management for the entire range of computer systems—from sensors to mobile devices to servers. However, efficient computing hardware design has become increasingly challenging, particularly for memories. This is due to the diminishing benefits from semiconductor technology scaling and increased demands from unprecedented data size of various data-intensive applications, such as videos and deep learning. Memory designers have developed different hardware-level design techniques to accommodate large amount of data (e.g., more-than-6T bitcells, assist techniques, error-correction-code (ECC)), which usually come with significant silicon area overhead or speed penalty. Although such overheads might be acceptable in general-purpose systems, they cannot satisfy the storage need of those data-intensive applications. This chapter presents an artificial intelligence (AI)-enabled efficient memory design methodology, which leverages AI techniques to extract useful data knowledge and actionable information for hardware designers, and ultimately to realize an intelligent-hardware design paradigm. The AI-enabled memory design methodology has been applied to video and deep learning applications, which enabled considerable power savings and significantly reduced hardware implementation cost as compared to state of the art. For future prospects, a research roadmap that contains open research problems is delineated for the broad research community.