With the rapid development of global urbanization and industrialization, the disposal of municipal sludge (MS) has rapidly inereased. Treating and disposing of MS quickly and efficiently has become the focus in the energy and environment field. Co-hydrothermal liquefaction (HTL) of MS is a promising technology for producing biocrude from wet biomass. It can inerease the biocrude yield, improve the biocrude quality, reduce the cost of HTL biomass, and facilitate large-scale production of biocrude. In this study, Box-Behnken design was employed to create 29 experimental groups. For the first time, process variables of co-HTL of MS and microalgae were modeled and optimized using two mathematical models, response surface method (RSM) and artificial neural network (ANN). The effects of typical operating, parameters on biocrude yield were investigated. The results showed that both ANN and RSM models had high accuracy and reliability in predicting co-HTL, with the ANN model having a goodness of fit of up to 0.9989. The reaction temperature and biomass/ratio had a significant contributing effect on the biocrude yield. Finally, a genetic algorithm combined with an ANN was used to predict the optimal process conditions for the co-HTL of MS and microalgae, and it showed a maximum biocrude yield of 32.2%.