This 2026 arXiv preprint introduces machine-learned adaptive protocols that dynamically optimize quantum teleportation components under bit-flip, amplitude damping, and depolarizing noise, yielding higher fidelity than standard Bell-state methods for both single- and two-qubit channels. As a computational simulation study without physical hardware validation or large-scale statistical sampling, the work demonstrates nontrivial decoherence compensation strategies but remains limited by idealized noise models and lacks testing on real quantum devices. The original coverage overlooks connections to broader quantum internet efforts, such as those outlined in the EU Quantum Flagship roadmap, where adaptive ML could integrate with error-corrected repeaters. Synthesizing this with Bennett et al.'s foundational 1993 protocol and recent Nature work on ML-driven quantum control reveals missed opportunities: these protocols hint at automated discovery of optimal algorithms that conventional feedback methods have not fully exploited, potentially accelerating scalable networks beyond current entanglement distribution limits.