To address the nonlinear channel issues caused by atmospheric turbulence and scattering effects in free space optical (FSO) communication systems and enhance the transmission reliability of optical communication systems in the sixth generation mobile communication (6G) era, this paper reviews the research progress of machine learning (ML) in improving FSO channel estimation. It compares and analyzes the applications of deep learning and non-deep learning methods in FSO channel estimation, demonstrating the advantages of ML in enhancing estimation accuracy and system performance. Finally, the challenges and future development trends of ML in FSO communication are discussed, highlighting the significant potential of ML algorithms in FSO systems and exploring future research directions.