Objective Traditional optimization algorithms exhibit lower training efficiency when training deep learning models due to increasing model parameters and deeper network layers. To address this issue a Nadabelief optimization algorithm based on Nesterov acceleration was proposed to improve the efficiency of model training. Methods Firstly the Adabelief algorithm was employed in place of the Adam algorithm to mitigate the generalization problem. Subsequently from the perspective of the first-order moment classical momentum term the Nesterov momentum acceleration mechanism was incorporated into the Adabelief algorithm. During gradient updates not only the gradient at the current moment was considered but the historical cumulative gradient was also utilized to adjust the magnitude of gradient updates so as to further improve the convergence of the algorithm. Finally the regret bound of the algorithm was obtained based on theoretical analysis to ensure the convergence of the algorithm. Results To verify the performance of the algorithm Logistic regression experiments were conducted in the convex scenario while image classification and language modeling experiments were carried out in the non - convex scenario. Comparisons with algorithms such as Adam and Adabelief demonstrated the superiority of the Nadabelief algorithm. Additionally the algorithm?? s robustness was confirmed by testing it at various initial learning rates. Conclusion The experiments demonstrate that the proposed algorithm not only maintains the generalization capability of the original Adabelief algorithm but also achieves better convergence accuracy. The proposed algorithm further improves the efficiency when training deep learning models.