Atomic force microscopy (AFM) provides a platform for high-resolution topographical imaging and mechanical characterization of a wide range of samples, including live cells, proteins, and other biomolecules. AFM is also instrumental for measuring interaction forces and binding kinetics for protein-protein or receptor-ligand interactions on live cells at a single-molecule level. However, performing force measurements and high-resolution imaging with AFM and data analytics are time-consuming and require special skill sets and continuous human supervision. Recently, researchers have explored the applications of artificial intelligence (AI) and deep learning (DL) in the bio-imaging field. However, the applications of AI to AFM operations for live-cell characterization are little-known. In this work, researchers implemented a DL framework to perform automatic sample selection based on the cell shape for AFM probe navigation during AFM biomechanical mapping. Researchers also established a closed-loop scanner trajectory control for measuring multiple cell samples at high speed for automated navigation. With this, we achieved 60x speed up in AFM navigation and reduced the time involved in searching for the particular cell shape in a large sample. Their innovation will directly apply to many bio-AFM applications with AI-guided intelligent automation through image data analysis together with smart navigation.