Cybersecurity is a critical trepidation in our digitalized world with an interconnected network. Due to the evolution of the Internet system and technology, equally the cyberthreats are keeping the global unsafe. Cybersecurity has become a global inevitability to protect systems from cyberthreats and ensure a protected online environment. Distributed denial of service (DDoS) assaults bombarded the attacks in the cloud environment. Finding a protection system against assaults is vital. The task of finding ensemble-based DDoS detection and investigating methods to protect network traffic by mitigating the assaults is proposed in a cloud environment. The system can efficiently recognize and respond to DDoS assaults by utilizing machine learning techniques and real-time data analysis, reducing their influence on network operations. Furthermore, assimilating anomaly detection and behavior analysis into the algorithm can develop its capability to differentiate between benign and intrusion activities. Reliable approaches are therefore desperately needed in order to identify and protect the network environment from such breaches before they have a chance to severely affect things. As a result, algorithms that utilize machine learning have become a viable method for detecting and thwarting DDoS attacks. Machine learning models may help businesses distinguish between regular and risky activities through investigating trends in network traffic and behavior, allowing them to take preemptive measures to secure their systems and networks. This study investigates and examines their potential influence on upgrading cybersecurity measures using a real-time environment using ensemble machine learning model. The research on detecting DDoS attacks has shown promising results in the establishment of cybersecurity measures with diverse machine learning algorithms. In addition to algorithm development, constant monitoring and updating of security controls and intrusion detection systems are imperious to stay ahead of evolving cyber threats. The security and dependability of networked systems remain to be gravely threatened by DDoS attacks. This study presents the development of a robust ensemble-based DDoS detection system, leveraging the rich and varied CICIDS2017 dataset. Our approach intricately combines the strengths of three powerful classifiers, Random Forest, Light GBM, and AdaBoost, employing an equal weighting strategy to combine to optimize decision making.