The rise of fake social media profiles presents significant challenges to online security, trust, and content authenticity. These fraudulent accounts are used for malicious purposes like misinformation, identity theft, phishing, and manipulation of engagement. Traditional detection methods, relying on text, image, or behavioral analysis independently, have limitations in distinguishing real and fake profiles. This study explores a multi-modal deep learning approach integrating Natural Language Processing (NLP) for text analysis, Convolutional Neural Networks (CNNs) for image classification, and machine learning models for behavioral analysis. Explainable AI (XAI) techniques are also incorporated to ensure interpretability in fake profile detection. The methodology uses web scraping tools like LangChain and BeautifulSoup to collect and preprocess profile data. The system is designed to address AI-generated content and adversarial attacks, improving detection through adversarial learning and real-time monitoring. A comparative study of machine learning and deep learning models evaluates their effectiveness in identifying fake profiles. Results show that a multi-modal approach outperforms single-modal methods in terms of accuracy and reliability. This research highlights the importance of integrating diverse data sources, optimizing detection techniques, and ensuring explainability for effective deployment. It contributes to safer and more secure digital interactions across social media platforms.