This study delves into the importance of micro-drilling in aluminum metal matrix composites (AMMCs), particularly within the aerospace and automotive sectors. It highlights the pursuit of lightweight and robust components for aircraft as well as fuel-efficient vehicles. A hybrid AMMC composed of AA7075 alloy reinforced with 5weight fraction of ZrB2 and 2 weight fraction of fly ash, using ultrasonic assisted stir casting method. Taguchi's design of experiments is employed to optimize the Material Removal Rate (MRR) and Tool Wear Rate (TWR). Advanced characterization techniques, such as Field Emission Scanning Electron Microscopy (FESEM) analysis, are utilized for assessing the machining process. Analysis of Variance (ANOVA) is employed to determine the significance of the model. The study also utilizes an Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid approach to optimize input parameters (Voltage, Capacitance, and Feed rate) to maximize MRR and minimize TWR. For the optimal output, the input parameters are 10µm/sec of feed rate, a 1nF of capacitance, and an 85V voltage.