Abstract: To protect one's wealth against inflation and economic volatility, gold is a vital financial asset. Nevertheless, a number of economic variables and market volatility make accurate gold price predictions difficult. Gold price forecasts made using more antiquated methodologies are notoriously inaccurate and unable to keep up with the ever-changing market. This research proposes a machine learning-based strategy for forecasting gold prices using past data and economic factors to solve this problem. To determine which machine learning algorithms are most successful in accurately predicting future gold prices, the suggested approach uses a battery of them. The research finds the best algorithm for predicting future prices by comparing the results of several models. In order to improve the accuracy of the forecasts, the implementation employs data preprocessing methods, feature selection, and predictive modeling. To help policymakers, financial analysts, and investors make educated judgments about gold investments, this study presents a data-driven method.

Keywords: Machine Learning, Gold Price Prediction, Economic Variables, Forecasting, Data Analysis

| DOI: 10.17148/IARJSET.2025.12473