The worlds hardest mineral is a diamond, which is 58 times harder than any other mineral, and its beauty as a jewel has long been appreciated. The diamond is popular due to its optical property as well as other causes such as its durability, custom, fashion, and strong marketing by diamond producers. Diamond demand, on the other hand, is not directly related to such inherent characteristics, but rather to their perceived value as rare and expensive objects. Forecasting diamond pricing is challenging due to non-linearity in important features such as carat, cut, clarity table, and depth. Given this, we conducted a comparative analysis and implementation of multiple supervised machine learning models in predicting diamond price in both classification and regression approaches. We evaluated eight different supervised algorithms in our work, including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron, and showcased the best suitable model given selected evaluation metrics. The analysis in this work is based on data preprocessing, exploratory data analysis, training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R2 score of 97.45% and an Accuracy value of 74.28%. As a result, the eXtreme Gradient Boosting method was recommended for forecasting the price of a diamond specimen. The diamond industry, renowned for its opulence and uniqueness, is driven by intricate pricing mechanisms influenced by a multitude of factors. This research proposal presents a novel approach to diamond price prediction through the integration of machine learning techniques. Recognizing the complexity of diamond valuation, this study seeks to develop predictive models that leverage data-driven insights to enhance the accuracy of diamond price estimations. Drawing upon a comprehensive dataset encompassing diamond attributes, market trends, and historical pricing, this research will explore the potential of machine learning algorithms to uncover hidden patterns and correlations within the data. By employing regression and ensemble techniques, our objective is to create predictive models that can capture the nuances of diamond pricing with a higher degree of precision than traditional methods. The anticipated outcomes of this research encompass not only the formulation of accurate predictive models but also the identification of the most influential factors affecting diamond valuation. In the context of a dynamic market landscape, the implementation of machine learning-driven price predictions has the potential to empower stakeholders, from jewelers to investors, with actionable insights for decision-making.  In conclusion, this research outlines a comprehensive plan to harness the power of machine learning for diamond price prediction. By merging technology and the intricacies of the diamond market, this study aspires to contribute to improved pricing strategies, informed investment choices, and a deeper understanding of the intricate world of diamond valuation.