To enhance the operational efficiency of wastewater treatment plants, this paper took effluent total nitrogen as the primary control indicator, comparing the predictive capabilities of mechanistic and machine learning models. Based on model construction, the improvement effects of parameter optimization on operational efficiency (effluent total nitrogen and energy consumption) were further investigated. First, the response surface methodology was coupled with the activated sludge model to establish a quadratic polynomial response surface for optimizing three key process parameters: dissolved oxygen in the aerobic zone, internal sludge recirculation ratio, and external sludge recirculation ratio. This optimization aimed to achieve the optimal effluent total nitrogen. In parallel, six classical machine learning models such as random forest model, extreme gradient boosting model, and light gradient boosting machine model were evaluated and selected. Furthermore, a multi-objective optimization framework was developed by coupling the Twin Delayed Deep Deterministic Policy Gradient Algorithm with a random forest model, synergistically optimizing effluent total nitrogen and energy consumption. The results demonstrate that the response surface methodology coupled with the activated sludge model achieves a high goodness of fit (R²=0.944 5). Under optimal conditions, the removal efficiency of effluent total nitrogen can reach 89.14%, with the optimal effluent total nitrogen concentration of 3.533 mg/L, representing a 45.48% reduction compared with that of actual wastewater treatment plants. Among the machine learning prediction models, the random forest model exhibits the best predictive performance (R²=0.79,E_MR=7.5%). After applying the multi-objective optimization control strategy processed by a Twin Delayed Deep Deterministic Policy Gradient Algorithm based on reinforcement learning, the average effluent total nitrogen concentration is 6.20 mg/L, 4.32% lower than that of actual wastewater treatment plants, while aeration and pumping energy consumption decrease by 33.12%, and the operating cost index is reduced by 25.16%. In the short term, the activated sludge model achieves superior performance for the single objective of total nitrogen removal. However, the advantage of the Twin Delayed Deep Deterministic Policy Gradient Algorithm optimization lies in its multi-objective optimization, delivering significant improvements in controlling energy consumption, cost, and stability while maintaining effluent total nitrogen compliance. In the long run, this approach provides superior overall treatment results. In conclusion, the Twin Delayed Deep Deterministic Policy Gradient Algorithm coupled with random forest model enables effective multi-objective optimization control and offers both theoretical and methodological support for the intelligent operation of wastewater treatment plants.