The rapid growth of memory-intensive workloads has exposed significant limitations in traditional homogeneous memory architectures. Emerging memory interconnect technologies, such as Compute Express Link (CXL) and Unified Bus (UB), enable flexible memory pooling across multiple computing nodes, but introduce heterogeneous memory latencies, leading to performance degradation for applications designed with uniform memory assumptions. Existing management approaches for these heterogeneous memory systems typically lack real-time performance-aware, making it difficult to dynamically balance application performance and memory resource utilization. This paper presents PAMM, a performance-aware adaptive memory management framework for heterogeneous memory environments enabled by advanced memory interconnects. By leveraging low-overhead runtime profiling and machine learning models, PAMM quantifies real-time application performance and anticipates potential slowdowns. Subsequently, it dynamically adjusts memory allocation strategies and places memory pages into appropriate tiers according to observed access characteristics. Evaluation shows that PAMM achieves substantial reductions in local memory usage while incurring minimal performance degradation.