The rapid expansion of social media platforms has elevated influencer marketing to a critical component of modern digital advertising strategies. Despite its growing importance, identifying suitable influencers using quantitative engagement metrics rather than superficial indicators such as follower count remains a major challenge for brands and marketers. This work presents a comprehensive Influencer Ranking and Engagement Analysis Platform developed using the MERN (MongoDB, Express.js, React.js, Node.js) stack to automate influencer discovery, evaluation, and ranking. The system collects real-time Instagram data via the Aptify API, extracting posts associated with specific hashtags to generate a dataset of “fresh” influencer activity. Key performance indicators, including likes, comments, and engagement rate, are computed and integrated into a proprietary Engagement Score algorithm that enables objective influencer ranking. A user-friendly dashboard provides dynamic visualizations, advanced filtering, and data export features to support decision-making. To ensure data confidentiality and integrity, the platform incorporates the Advanced Encryption Standard (AES) algorithm for secure storage of sensitive information. By transforming raw social media data into actionable insights, the proposed platform offers a scalable, data-driven solution that enhances the efficiency, accuracy, and Return on Investment (ROI) of influencer marketing campaigns.