The environmental threat, pollution and damage posed by heavy metals to air, water, and soil emphasize the critical need for effective remediation strategies. This review mainly focuses on microbial electrochemical technologies (MET) for treating heavy metal pollutants, specifically includes Chromium (Cr), Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb), Nickel (Ni), and Cobalt (Co). First, it explores the mechanisms and current applications of MET in heavy metal treatments in detail. Second, it systematically summarizes the key microbial communities involved, analyzing their extracellular electron transfer (EET) processes and summarizing strategies to enhance the EET efficiencies. Next, the review also highlights the synergistic microbial interactions in bioelectrochemical systems (BES) during the recovery and removal (remediation) processes of heavy metals, underscoring the crucial role of microorganisms in the transfer of the electrons. Then, this paper discussed how factors including pH values, applied voltages, types and concentrations of electron donors, electrode materials, biofilm thickness and other factors affect the treatment efficiencies of some specific metals in BES. BES has shown its great superiority in treating heavy metals. For example, for the treatments of Cr6+, under low pH conditions, the recovery and removal rate of Cr-6(+) by double chambers microbial fuel cell (DCMFC) can generally reach 98-99%, with some cases even achieving 100% (Gangadharan & Nambi, 2015). For the treatments of heavy metal ions such as Cu2+, Zn2+ and Cd2+, BES can also achieve the rates of treatments of more than 90% under the corresponding conditions of appropriate pH values and applied voltages(Choi, Hu, & Lim, 2014; W. Teng, G. Liu, H. Luo, R. Zhang, & Y. Xiang, 2016; Y. N. Wu et al., 2019; Y. N. Wu et al., 2018). After that, the review outlines the future challenges and the research opportunities for understanding the mechanisms of BES and microbial EET in heavy metal treatments. Finally, the prospect of future BES researches are pointed out, including the combinations with existing wastewater treatment systems, the integrations with the wind energy and the solar energy, and the application of machine learning (ML) in future BES. This article has certain significance and value for readers to better understand the working principles of BES and better operate and control BES to deal with heavy metal pollutants.