With accelerating global urbanization and industrialization, the continuous expansion ofurban population size and industrial production capacity leads to increasingly severe challenges inmunicipal sludge (MS) treatment. Traditional disposal methods such as landfill and composting facebottlenecks including land resource constraints, greenhouse gas emissions, and heavy metalcontamination, while incineration suffers from technical issues such as high energy consumption anddioxin pollution. In this context, developing sludge valorization technologies that are bothenvironmentally friendly and economically feasible has become a critical research focus. Hydrothermalliquefaction (HTL) technology is regarded as one of the most promising sludge treatment technologiesdue to its ability to directly process biomass with high moisture content (80% - 90%). This studyinnovatively adopts the co-HTL strategy that combines municipal sludge and microalgae, achievingsynergy through a feedstock formulation strategy. This approach increases biocrude yields, improvesEnergy Environmental Protectionproduct quality, and reduces the cost of biomass HTL technology, thus facilitating industrial-scaleapplication. The Box-Behnken Design (BBD) was used to develop a three-factor, three-level responsesurface model, selecting reaction temperature (280 - 340 ℃), residence time (15 - 45 min), andbiomass-to-water mass ratio (1∶5 - 1∶15) as key variables. Through 29 sets of experiments, theinfluence mechanisms of process parameters on biocrude yield were systematically investigated. Thisstudy introduces a novel dual-model comparative analysis framework, integrating response surfacemethodology (RSM) and artificial neural network (ANN). The RSM established a predictive modelbased on a second-order polynomial equation, achieving an R value of 0.983 3 and demonstratingexcellent linear fitting capability. In contrast, the ANN employed a three-layer topological structure (3-node input layer, 10-node hidden layer, and 1-node output layer). After training with the Levenberg-Marquardt algorithm, the model′s R significantly improved to 0.998 9, demonstrating the superiority ofneural networks in modeling nonlinear complex systems. An increase in temperature significantlypromotes biomass decomposition, but secondary reactions (condensation/gasification) above 325 ℃lead to a decline in biocrude yield. Prolonged residence time results in only marginal yieldimprovement, while excessive residence time under high-temperature conditions tends to induce sidereactions. At lower temperatures, a longer residence time is required to ensure complete reactions,whereas at higher temperatures, the optimal residence time is shorter. Increasing the biomass-to-waterratio from 0.08 to 0.25 g/mL enhances yield by 5% - 7%, yet excessively high ratios may reduceintermediate solubility and inhibit oil phase formation. High sludge ratios significantly suppress yield,primarily because the high ash content (59.1%) dilutes the organic biomass concentration. Finally, agenetic algorithm combined with an ANN was used to predict the optimal process conditions for the co-HTL of MS and microalgae, achieving a maximum biocrude yield of 32.2%. This study offers aninnovative solution for sludge resource utilization, and its process optimization framework can beapplied to other fields involving the collaborative conversion of organic solid wastes.