The challenge of graph vertex coloring is a well-established problem in combinatorial optimization, finding practical applications in scheduling, resource allocation, and compiler register allocation. It revolves around assigning colors to graph vertices while ensuring adjacent vertices have distinct colors, to minimize the total number of colors. In our research, we introduce an innovative methodology that leverages machine learning to address this problem. Our approach involves comprehensive preprocessing of a collection of graph instances, enabling our machine learning model to discern complex patterns and relationships within the data. We extract various features from the graph structures, including node degrees, neighboring node colors, and graph density. These features serve as inputs for training our machine learning model, which can encompass neural networks or decision trees. Through this training, our model becomes proficient at predicting optimal vertex colorings for previously unseen graphs. To evaluate our approach, the authors conducted extensive experiments on diverse benchmark graphs commonly used in vertex coloring research. Our results demonstrate that our machine learning-based approach achieves comparable or superior performance to state-of-the-art vertex coloring algorithms, with remarkable scalability for large-scale graphs. Further, in this research, the authors explored the use of Support Vector Machines (SVM) to predict optimal algorithmic parameters, showing potential for advancing the field. Our systematic, logical approach, combined with meticulous preprocessing and careful optimizer selection, strengthens the credibility of our method, paving the way for exciting advancements in graph vertex coloring.