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In contemporary high-speed networking landscapes, standard isolated tracking setups encounter prominent functional bottlenecks, frequently yielding excessive false alarms while letting complex zero-day exploits slip through unnoticed. This investigation counters these operational shortfalls by rolling out a responsive, dual-stage traffic classification mechanism that purposefully blends supervised algorithmic tracking with deep unsupervised behavioral grouping. The foundational layer leverages a refined tree ensemble to swiftly screen and isolate known malicious patterns with minimal delay. In a complementary fashion, unclassified packet streams are re-routed to a secondary unsupervised filter that calculates distance-driven spatial variance to uncover structural anomalies indicative of undocumented exploits. This collaborative framework underwent empirical validation on two distinct network traffic benchmark suites: the CICIDS 2017 profiles and the UNSW-NB15 environments. The practical trials confirm notable diagnostic gains, hitting peak detection efficiencies of 99.24% and 95.38% on the respective datasets. Crucially, false alarm probabilities dropped to 0.42% and 1.15%, affirming that this dual-tier methodology establishes excellent computational stability and strong transactional scaling for enterprise network boundaries.