This study presents an advanced co-simulation framework for the optimal design and control of a renewable-integrated District Heating (DH) system. The framework leverages a Python-based model alongside an established DH network model to facilitate data-driven optimizations and interfaces for sustainable energy transitions. The framework integrates TRNSYS for dynamic system modeling of DH network model and Python for system design, control optimization, and impact assessments. The use case focus-es on a medium-sized residential community, where energy system con-sists of PV, solar thermal collectors, a water-to-water heat pump, seasonal thermal storage, domestic hot water tanks, and natural gas auxiliary heaters. The co-simulation enables Multi-objective optimization and a TOPSIS-based multi-criteria decision-making approach, where an approximate 15% reduction in net present cost reduction and an 11% decrease in environmental impact were achieved. The developed data-driven controls minimized comfort deviations, with deviations less than −0.5 K for domes-tic hot water (DHW) and − 0.5 K for space heating (SH), significantly out-performing the Rule-Based Controller (RBC), which had larger deviations. Additionally, the data-driven control approach decreased environmental impacts by about 1.6%, primarily through optimized solar energy use. These findings highlight the future role of advanced hybrid co-simulation frameworks in enhancing DH system design and control, where different types of distributed renewables are expected to be integrated.