Microrobotics, particularly in the field of biomedicine, has garnered considerable attention due to its potential for noninvasive medical interventions enabled by the small size of microrobots. However, controlling and imaging them present unique challenges compared to their macroscale counterparts, primarily due to the intricate anatomical spaces and dynamic environments within the human body. Existing imaging modalities also face limitations, hindering real-time visualization and control of microrobots in deep tissue. Machine learning (ML) algorithms offer promising solutions to these challenges by enabling adaptive motion control and enhancing image resolution through robust data analysis and decision-making capabilities. In this review, a comprehensive overview of recent advancements in ML-based techniques for microrobotic research is provided, emphasizing their applications in imaging and control in biomedical contexts. Additionally, current obstacles and potential future directions for ML algorithms in microrobotics, particularly regarding their translation to clinical settings, are discussed.