In the rapidly evolving landscape of cybersecurity, the increasing sophistication of cyberattacks necessitates the development of intelligent and adaptive defense mechanisms. Traditional security systems often struggle to effectively detect and mitigate novel and complex attacks due to their reliance on predefined rule-based techniques. This paper proposes a hybrid approach integrating Fuzzy Logic and Deep Learning for cyberattack detection and prevention, aiming to enhance the accuracy and adaptability of security systems. The proposed framework leverages Fuzzy Logic to handle uncertainty in network traffic, enabling real-time decision-making with flexible rule sets. Meanwhile, Deep Learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are utilized to analyze patterns in large-scale cybersecurity datasets, improving threat identification. The fusion of these technologies ensures an adaptive, self-learning, and robust defense mechanism against cyber threats, including DDoS attacks, malware, and insider threats. Extensive simulations and real-world datasets validate the effectiveness of the proposed system, demonstrating superior detection accuracy compared to conventional methods. This study contributes to the advancement of AI-driven cybersecurity by providing an intelligent, scalable, and interpretable cyber defense framework, suitable for modern network infrastructures.