Denial-of-Service (DoS) attacks continue to challenge the stability of networked systems, exploiting vulnerabilities across band- width, protocols, and application layers. This paper delves into the dynamics of DoS attacks, emphasizing their operational mechanisms and impacts, such as bandwidth exhaustion, server resource depletion, and application-level disruptions. A comprehensive overview of detection methodologies is presented, including anomaly detection with machine learning algorithms, signature-based analysis, and real-time traffic monitoring tools like Wireshark and Snort. Mitigation strategies are analyzed, focusing on innovative techniques such as rate limiting, sinkholing, and third-party anti-DoS services for minimizing attack repercussions. Performance evaluations highlight the superiority of machine learning models, such as Random Forest, in achieving high accuracy and minimal latency for real-time attack detection and response. This study underscores the importance of integrating advanced detection frameworks with proactive mitigation approaches to enhance network resilience against persistent DoS threats.