Developing economically sustainable CO2 capture and conversion processes is essential to realize carbon neutrality. This study proposed an integrated process for CO2 capture and conversion-to-methanol (CCTM) and applied machine learning-based optimization to enhance techno-economic-environmental performance. After validating CO2 capture and CO2-to-methanol sections, an advanced CCTM design was developed and compared with conventional one regarding techno-economic-environmental performance across various operating scenarios. The advanced CCTM exhibited significant improvements in energy consumption (14.73–16.30%), production cost (0.81–1.28%), and net CO2 reduction (3.13–3.38%) owing to efficiently reusing waste heat, off-gas, and water resources. The one-at-a-time sensitivity analysis revealed roles of each variable and nonlinear variable-performance tendencies among operating variables in the advanced CCTM process. Subsequently, a well-developed deep neural network (DNN) model precisely formulated the relationship between key variables and performances. The DNN-based optimization provided optimum operating conditions within a minute, resulting in an 8.21 $/tMeOH (∼0.81%) reduction in production cost compared to base case of CCTM. Notably, the total CO2 capture rate of 92.53% at an optimal condition highlighted the significant contribution of advanced CCTM to carbon neutrality. The findings provide a viable reference for the effective and sustainable design and operation of an integrated CCTM process.