Path planning algorithms are indispensable for controlling micro-/nanorobots through complex and unknown environments in the biomedical and medical fields. With the tasks performed becoming more complex, higher-quality paths are required to avoid obstacles for ensuring the safe and efficient movement of micro-/nanorobots. A comparative analysis of path planning algorithms is conducted to elucidate the algorithm’s application and optimization for different environments. According to the environment modeling approach, existing path planning algorithms are classified into searching, sampling, and dynamic aspects. Searching path planning algorithms directly retrieve the global path possessing minimum cost from the modeled static waypoints. Sampling path planning algorithms employ randomly sampled waypoints within the target space, which eliminates the necessity for environmental modeling. Dynamic path planning algorithms utilize local paths to regulate the motion of micro-/nanorobots in real time. Deep learning networks based on big data will become an important research direction for the control and navigation of micro-/nanorobots. The advantages and limitations of path planning algorithms in varied spatial contexts are elucidated through detailed examples and descriptions, providing a comprehensive understanding of performance and applicability. This review underscores recent advancements in this emerging domain and stands as a testament to the dynamic landscape of micro-/nanorobotics and the continual pursuit of superior motion control solutions.