Acid mine drainage (AMD) caused environmental risks from heavy metal pollution, requiring treatment methods such as chemical precipitation and biological treatment. Monitoring and adapting treatment processes was crucial for success, but cost-effective pollution monitoring methods were lacking. Using bioindicators measured through 16S rRNA was a promising method to assess environmental pollution. This study evaluated the effects of AMD on ecological health using the ecological risk index (RI) and the Risk Assessment Code (RAC) indices. Additionally, we also examined how acidic metal stress affected the diversity of bacteria and fungi, as well as their networks. Bioindicators were identified using linear discriminant analysis effect size (LEfSe), Partial least squares regression (PLS-R), and Spearman analyses. The study found that Cd, Cu, Pb, and As pose potential ecological risks in that order. Fungal diversity decreased by 44.88% in AMD-affected areas, more than the 33.61% decrease in bacterial diversity. Microbial diversity was positively correlated with pH (r = 0.88, p = 0.04) and negatively correlated with bioavailable metal concentrations (r = 􀀀 0.59, p = 0.05). Similarly, microbial diversity was negatively correlated with bioavailable metal concentrations (bio_Cu, bio_Pb, bio_Cd) (r = 0.79, p = 0.03). Acidiferrobacter and Thermoplasmataceae were prevalent in acidic metal environments, while Puia and Chitinophagaceae were identified as biomarker species in the control area (LDA>4). Acidiferrobacter and Thermoplasmataceae were found to be pH-tolerant bioindicators with high reliability (r = 1, P < 0.05, BW > 0.1) through PLS-R and Spearman analysis. Conversely, Puia and Chitinophagaceae were pH-sensitive bioindicators, while Teratosphaeriaceae was a potential bioindicator for Cu–Zn–Cd metal pollution. This study identified bioindicator species for acid and metal pollution in AMD habitats. This study outlined the focus of biological monitoring in AMD acidic stress environments, including extreme pH, heavy metal pollutants, and indicator species. It also provided essential information for heavy metal bioremediation, such as the role of omics and the effects of organic matter on metal bioavailability.