<p>This study investigates the influence of two Aluminium-based alloy compositions on machinability and mechanical properties to optimize turning process parameters for industrial performance and sustainability. Two alloys were fabricated using die casting, with Sample A consisting of 85% Aluminium, 10% zinc, and 5% manganese, and Sample B consisting of 78% Aluminium, 5% fly ash, 5% Silicon Carbide, 2% manganese, and 10% zinc. A D-optimal design with a reduced quadratic model for 12 randomized runs was employed, and optimization was conducted using Fuzzy AHP for weight determination and Fuzzy WASPAS for decision analysis. Process parameters included cutting speed, feed rate, and depth of cut, while response factors measured were surface roughness, material removal rate, tool wear, power consumption, geometric accuracy, and surface hardness. Sample B, with fly ash and Silicon Carbide reinforcements, exhibited superior performance by achieving better surface finish, tool life, and efficiency, making it suitable for advanced machining applications. These findings align with SDG 12 by promoting the use of sustainable materials such as fly ash, reducing waste, and optimizing resource consumption. Optimized machining parameters support SDG 7 and SDG 13 by lowering energy consumption and minimizing carbon emissions. Enhanced productivity and efficiency contribute to SDG 8 and SDG 9 by improving manufacturing processes and fostering innovation in material usage. The study is limited to two alloy compositions and one casting method, suggesting further research on additional alloys and fabrication techniques. This research offers a novel approach to optimizing machining parameters, advancing sustainable manufacturing practices through efficient resource utilization and reduced environmental impact.</p>