In this thesis we address the demand for fast, accurate and compact similarity search to enable data-intensive and edge AI applications using our novel content addressable memory (CAM) designs. CAMs, with their ability to perform parallel in-memory searches, offer the potential to replace traditional search schemes. Despite these benefits there are many challenges for using CAMs due to scaling issues at advanced nodes and limited search resolution at larger Hamming Distances. In this thesis we highlight these challenges by analyzing CAMs at the 7nm technology node using layout extracted parasitics and considering various variability sources. We study these effects at the application-level using a CAM-based recommendation system. We suggest design modifications at the algorithmic, schematic and layout-levels to solve these issues to enable the use of CAMs in data-intensive AI applications. To enhance the use of hyperdimensional computing (HDC) for edge applications, we develop novel energy-efficient and variation-tolerant CAM and in-memory encoding designs using resistive memory devices at the 22nm node. We use an HDC-based speech recognition system to evaluate and benchmark our designs against other technologies. Overall, in this thesis, we focus on developing efficient CAM designs tailored to meet the varying requirements of diverse AI systems.