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As a result of the implementation of artificial intelligence, there is a possibility that the existing organizational structures and management procedures will go through significant transformations (AI). Conventional organizational structures as well as the decision-making processes of companies are being completely upended by AI. A backpropagation neural network (BPNN) that is based on AI digitizing technology is used in the construction of a salary prediction model (SPM), which is then subjected to the Nesterov and Adaptive Moment Estimation (Nadam) approach for the purpose of further enhancing its accuracy. The end output is known by its full title, which is a salary prediction model (SPM). The results of this study have the potential to have a positive impact on the HRM process, the amount of work that human resource managers have to do, and the overall efficiency of professions. According to the findings, the Nadam optimization technique offered the highest degree of optimization performance as well as the fastest convergence. The duration of the training was exactly 186 seconds, and the final score for the expected outcome was 0.75 percent. Further proof of the SPM's validity may be seen in the remarkable learning performance as well as the accuracy rate of up to 79.4% achieved by Nadam's BPNN-based SPM optimization. The outcomes of the research might potentially serve as a guide for the development of data mining-based HRM solutions in the future.