###### Abstract
Nonetheless, inverse models are regarded as an optimizing issue in which the outcome or the specified attributes are recommended to be minimized or maximized as a function of composition or structural factors [23; 24]. In addition, various machine learning approaches are employed to shed light on the intricate network of processing, structural attributes, and performing interactions that define the heart of material evolution depending on the chemistry, fundamental physics, and the engineering of materials [25; 26]. In the realm of metal manufacturing, ML approaches have a vast capacity for the creation of either inverse or forward models. Typical uses involve process optimization, flaw detection, and microstructure characterization, as well as attribute estimation of the end output [27; 28; 29]. Because of the widespread incorporation of Al\({}_{2}\)O\({}_{3}\), TiC, SiC, and B4C particles into the metallic matrix, ML has already been extensively applied in MMCs [24; 30]. The mechanical properties of AMCs with various additions, including Al/Al\({}_{2}\)O\({}_{3}\)[31; 32; 33], AA2219/Al\({}_{2}\)O\({}_{3}\)/TiC [34], A356/Al\({}_{2}\)O\({}_{3}\)[35], A356/B4C [36], AA6061/Al\({}_{2}\)O\({}_{3}\)/SiC [37], and Al-Si-Mg/Al\({}_{2}\)O\({}_{3}\)/SiC [38; 39], have all been predicted using ML. Adding reinforcement particles to the base metal makes it more complicated. Realizing the connections among variables such as the reinforcement volume proportion and particle size, alloy chemical structure, interfacial bonding, processing and post-processing factors, and final product mechanical qualities is challenging as a result. Other statistical techniques besides ML, such as response surface methodology, have been extensively adopted in the literature when dealing with AMCs. The development and further exploration of pertinent mathematical ideas are essential requirements for today's data modeling. In previous research, response surface methodology, often known as RSM, has been utilized to enhance PM processes, notably those involving AMCs. Tests can be replicated and improved upon with the help of RSM, which is a collection of empirically based statistical and mathematical methodologies [40; 41; 42; 43]. Moreover, genetic algorithms (GAs) can be utilized in optimization to steer clear of reaching local optimum answers. GA methodology is extensively utilized in a broad range of business and research domains [44; 45]. In addition to producing the most effective algorithms for each individual set, the convergence of the findings is also ensured by GAs, which make use of certain criteria in order to arrive at a solution that strives to achieve a global minimum for a fitness function [46; 47; 48; 49]. The RSM and artificial neural network (ANN) techniques were utilized in a study that [PERSON] et al. [50] conducted to examine the impact of Al/SiC composites on the microhardness (VHN). AMCs that were strengthened with an x-weight percentage of SiC were produced using PM. The values for x varied, with 5, 7.5, and 10 microparticles. The scientists ended up deciding that the composite with a reinforcement ratio of 7.5% exhibiteda superior sintered density and Vickers microhardness because of the uniform dispersion of the filler particles inside the Al matrix, which did not include any pores. In addition to this, the findings indicate that the ANN approach achieved more precise results than the RSM method. The authors of the [PERSON] et al. [51] study showed the mechanical behavior of Al-7075 that was reinforced with SiC and TiC using the PM method. SiC and TiC were mixed together with a variety of different weight proportions after the design matrix was generated with RSM. The scientists came to the conclusion that the mechanical properties of the outcome were improved due to the content of the composite, which consisted of 90% Al 7075, 4% TiC, and 8% SiC. Therefore, the aim of this study was to conduct a comprehensive investigation into the modeling of various parameters' effects on AMCs, specifically Al-4 Cu alloy.