This study investigates user engagement on Instagram using “Instagram Reach Analysis Data,” focusing on key metrics such as likes, comments, shares, and views to understand content performance and user interaction patterns. We employed Python-based data analysis, utilizing Pandas for data extraction and visualization tools, including histograms and scatter plots, to interpret the data. Additionally, we used NetworkX for network analysis to explore the impact of different sources of impressions such as home feeds, hashtags, and the explore page on total reach. Through correlation analysis and graph-based visualizations, we uncovered intricate relationships among various engagement metrics. Centrality measurements further identified the most influential factors within the engagement network. Our findings emphasize the importance of optimizing strategies for home feed content and hashtag usage, providing valuable insights for content creators. This research enhances social media analytics by offering practical guidance to improve Instagram engagement.