The chemical recycling of polycotton textile waste via glycolysis offers an effective approach for recovering terephthalic acid (TPA) from mixed-fiber waste streams. This study offers a process-level life cycle assessment (LCA) of polycotton solvolysis utilizing a mixed ethylene glycol and propylene glycol (EG+PG) solvent system. The LCA was evaluated across a response surface experimental design that spanned temperature (160-180°C), reaction time (10-30 min), solid-liquid ratio (10-20 wt.%), and KOH catalyst loading (0.5-2.5 wt.%). The functional unit is defined as 1 kg of polycotton waste (50% PET, 50% cotton) that has been treated under various process conditions. The ecoinvent 3.12 background database was employed to evaluate environmental impacts, with a cut-off approach for waste textile input, using the Environmental Footprint 3.1 method. According to the results, the solid-liquid ratio was the most critical process parameter, with climate change implications spanning from 1.8 to 4.2 kg CO₂-eq per functional unit. Due to the proportionally higher solvent requirements per kilogram of fiber treated, the conditions at SL 10% resulted in the highest climate change burdens, while the SL 20 wt.% conditions attained the lowest values. The primary factor influencing freshwater ecotoxicity was the chloride emissions associated with the production of propylene glycol. While mechanical recycling of PET reports climate change impacts of 0.7 to 1.6 kg CO2-eq per kg, the optimized solvolysis condition achieved 1.8 kg CO2-eq per kg polycotton, representing a significant improvement over previously reported glycolysis-based chemical recycling values of 4.3 to 5.8 kg CO2-eq per kg, which ranged from 124.7 to 251.4 CTUe in magnitude. The optimal environmental condition was 180°C, 30 minutes, SL 20 wt.%, and 2.5 wt.% KOH, which resulted in a 52.7% dissolved yield at 1.8 kg CO2-eq. These results indicate that the most effective method for reducing the environmental impact of polycotton chemical recycling under varying process conditions is to manage solvent quantity through solid-liquid ratio optimization.