The rise of botnets presents a continually increasing threat to the infrastructure of traffic networks. However, detection mechanisms capable of coping with such sophisticated attack patterns remain very limited. This research explores the behavioral characteristics and impact of botnets within traffic networks, which have evolved from traditional Distributed Denial of Service (DDoS) attacks towards more complex, multi-vector assault strategies. Used a hybrid methodology consisting of machine learning algorithms combined with network flow analysis across 18 months of aggregated traffic data from 15 major network operators. Our analysis adopted a new deep learning framework integrating temporal-spatial features with behavioral fingerprinting to identify botnet signatures. The results are also instrumental in revealing that modern botnets display distinctive patterns of traffic: 73% exhibit adaptive command-and-control structures, 61% use encrypted communication channels, and 84% demonstrate the capability to modify real-time attack vectors. Additionally, identified a growth of 47% of botnet-driven traffic anomalies detected by our solution and unnoticed by traditional intrusion detection systems. These findings led to the development of a new classification framework for botnet detection. It achieved up to a 91.3% accuracy rate along with a false positive rate of 0.8%. It is meant to contribute to the understanding of the botnet evolution and provide network operators with improved detection methodologies regarding the emergent botnet behaviors, especially in complex traffic environments.