Objective To construct a grey prediction model based on bacterial drug resistance data of methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB), third-generation cephalosporin-resistant Escherichia coli (3GCR-E. coli), and third-generation cephalosporin-resistant Klebsiella pneumoniae (3GCR-KP), analyze the trends in bacterial drug resistance characteristics, and explore the application value of grey prediction model in the field of bacterial drug resistance. Methods A grey prediction model GM(1, 1) was constructed based on drug resistance rate data of MRSA, CRPA, CRAB, 3GCR-E. coli, and 3GCR-KP from the national bacterial drug resistance surveillance reports in 2014-2018. The precision of the model was assessed with posterior error ratio (C) and the small error probability (P). The fitting effectiveness of the model was evaluated with relative error and grade ratio deviation. The prediction effectiveness of the model was verified with the data from 2019 to 2020. Final prediction of drug resistance rates from 2021 to 2023 was made based on the constructed model. Results The GM (1, 1) model constructed in this study has good prediction effectiveness on drug resistance rates of MRSA, CRPA, CRAB, 3GCR-E. coli and 3GCR-KP. According to this model, resistance rates of the above bacteria were predicted to be reduced to 23.9%, 15.2%, 50.2%, 43.8%, and 26.1% respectively by 2023. Conclusion The control measures taken against bacterial drug resistance in China have achieved remarkable results. GM (1, 1) model is effective in predicting bacterial drug resistance rate and can be promoted for the application in the field of bacterial drug resistance management.