Network traffic classification is essential to managing and optimizing modern network environments. However, traditional methods like port-based analysis and deep packet inspection often face scalability and performance limitations challenges. This chapter proposes an innovative context-aware eBPF-assisted AI (BAI) solution to address these challenges and enable efficient and accurate smartphone traffic classification. Our approach leverages the capabilities of the extended Berkeley Packet Filter (eBPF) to reduce packet processing overhead and enhance performance. By dynamically attaching BPF programs, our BAI solution enables targeted and context-aware traffic classification in smartphones. We introduce a power-efficient approach that selectively processes interested app traffic, optimizing resource utilization and enhancing classification efficiency. Experimental evaluations demonstrate the effectiveness of our method, showing significant reductions in packet processing time (up to \(93.6\%\) compared to the legacy approach) and CPU utilization. Integrating eBPF and BPF maps enables real-time, low-latency traffic classification with improved accuracy and scalability. Our proposed BAI solution offers a robust and versatile approach to traffic classification, addressing the limitations of traditional methods and providing a foundation for further advancements in network monitoring and optimization.