This study focuses on the optimisation of the injection moulded Polypropylene-Sawdust composite. The Polypropylene material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the design of experiments using the central composite design (CCD). The two screw plunger injection moulding machine was used to produce Polypropylene-Sawdust composite at various temperature. The produced composite was evaluated for its mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology (RSM) and artificial neural networks (ANN) were used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced Polypropylene-sawdust composite. The models were validated using coefficient of determination (R 2), the coefficient of determination (R 2) obtained ranged from 0.9435 (94.357%) to 0.9988 (99.88%) which indicates that a substantial good fit was achieved by the developed models. A desirability of 0.952 was obtained which shows the adequacy of the model terms the optimization results for Polypropylene-Sawdust composite shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with a values of 31.90 MPa, 41.94 MPa, 88.22 MPa and 2.72 GPa respectively which was obtained at barrel temperature of 224.65 o C. Percentage elongation and average deflection were minimized with a values of 74.12% and 6.46 cm respectively. The artificial neural networks gave the optimal of the two examined models.