The rapid spread of fake news and deceptive content presents a substantial challenge to maintaining information credibility in the digital era. This paper introduces an integrated framework combining sentiment analysis, text mining, and finite automata to detect fake news and deceptive narratives effectively. Sentiment analysis offers valuable insights into the emotional undertones of text, while text mining facilitates the identification of meaningful patterns within extensive datasets. Finite automata enhance this process by efficiently identifying predefined linguistic patterns associated with deception. The proposed approach underscores the efficacy of leveraging linguistic, computational, and algorithmic methodologies to address the intricate challenges of automated fake news detection.