Plasma arc cutting is a non-traditional thermal cutting technology utilized in modern manufacturing industries to create a variety of products at a lower cost. It can cut a variety of electrically conductive materials. In Amhara Metal and Machine Industry technology and Development Enterprise cutting of plate with CNC plasma arc cutting machine is difficult, due to the combined effect of a continuous failure of the nozzle and getting low quality, this quality phenomenon included high surface roughness and low material removal rate. This study aimed to optimize process parameters like cutting current, cutting speed, gas pressure, and standoff distance of a Plasma arc cutting machine that affect the material removal rate, surface roughness, and nozzle diameter change after cutting performed. These process parameters have an influence on the quality of the machined part and the effect of these process parameters on material removal rate, surface roughness, and nozzle diameter change after cutting using a 60×30×10 mm mild steel was investigated. The experiment was designed using Taguchi's L27 orthogonal array to reduce the number of experimental runs, and it was carried out using an EDON CUT 100 CNC plasma arc cutting machine from Amhara Metal and Machine Industry Technology and Development Enterprise. The parameters of the plasma arc cutting process were optimized utilizing a hybrid Artificial Neural Network and Genetic Algorithm. For optimal weight, an ANN model was created and trained with GA based on the input and output result values. This objective function was further optimized by GA for obtaining an optimum result. From the analysis of the result plasma arc cutting parameters 73.9764A cutting current, 444.325 mm/min cutting speed, 0.7998 MPa gas pressure, and 3.1185 mm standoff distance with their response value of 7.0032 g/sec material removal rate, 4.2062 µm surface roughness, and 1.3142 mm nozzle diameter were obtained as an optimum value. Finally, a confirmation test was carried out by cutting three specimens with the optimal process parameters. The measured responses revealed that the 6.9280g/sec material removal rate, 4.3499 m surface roughness, and 1.3702 mm nozzle diameter had an error of 0.05 to 0.14 from the predicted optimal value, and that the responses with this optimal value were better than the previous cutting combination of the enterprise. Keywords: Plasma arc cutting machine, optimization of plasma arc cutting parameters, Artificial Neural Network, Genetic Algorithm