Network attacks pose a significant threat to the security and integrity of computer networks. The ability to predict and prevent these attacks is crucial for maintaining a secure network environment. Supervised machine learning techniques have emerged as effective tools for network attack prediction due to their ability to analyze large amounts of network data and identify patterns indicative of malicious activity. We present a comprehensive analysis of supervised machine learning techniques for the prediction of network attacks. We collect and pre-process the data, extracting relevant features and transforming them into a suitable format for machine learning algorithms. We evaluate the performance of these algorithms. We investigate the interpretability of the trained models to gain insights into the underlying patterns and characteristics of network attacks. This allows network administrators to understand the nature of attacks and develop appropriate defenses strategies. Additionally, we discuss the challenges and limitations associated with the application of supervised machine learning techniques in the domain of network attack prediction, such as the need for real-time analysis and the emergence of sophisticated evasion techniques.