This comprehensive investigation uses powerful NLP and multi-modal integration to detect fake information on social media. Our technique was tested using 10,000 news pieces from various social media networks. Advanced NLP models are crucial, with the state-of-the-art transformer model, BERT, collecting subtle contextual information with 94.2% accuracy. SVM and BERT had the greatest classification accuracy at 95.6% when combined with ensemble approaches. Multi-modal information sources including picture captions and user metadata improved classification performance, with the textual-image model achieving the greatest accuracy at 96.8%. The research was ethical, incorporating fairness analysis and transparency measures, to ensure detection trustworthiness and fairness. These findings show that sophisticated NLP models and multi-modal integration can improve fake news detection, enabling the creation of strong, real-world apps to combat disinformation and protect digital discourse.