<![CDATA[Social media platforms generate massive volumes of publicly accessible digital data that reflect organizational competitive strategies, yet most existing competitor analyses remain manual, descriptive, and limited to surface-level engagement metrics, resulting in low scalability and weak strategic intelligence. This study proposes a Machine Learning Enabled Social Media Competitive Intelligence System designed to automate competitor strategy extraction through artificial intelligence and big data analytics. The objective is to develop a computational framework capable of identifying strategic content patterns, communication objectives, audience positioning, and paid advertising behaviors using data-driven techniques. Large-scale public data from social media posts, engagement indicators, and advertising transparency libraries are collected and processed through data preprocessing pipelines, including text normalization, tokenization, and feature extraction using TF-IDF and word embedding representations. Supervised machine learning algorithms are implemented to classify content themes, detect strategic clusters, and model competitive positioning patterns, while performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robustness and reliability. Experimental findings demonstrate that the proposed system significantly enhances analytical consistency, scalability, and strategic insight generation compared to traditional mixed method approaches. This research contributes to the advancement of AI-driven social media analytics and establishes a computational foundation for scalable big data-based competitive intelligence systems aligned with Artificial Intelligence and Big Data domains.]]>
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