In　the　rapidly　growing　Internet　of　Things　(IoT)　landscape,　federated　learning　(FL)　plays　a　crucial　role　in　enhancing　the　performance　of　heterogeneous　edge　computing　environments　due　to　its　scalability,　robustness,　and　low　energy　consumption.　However,　one　of　the　major　challenges　in　such　environments　is　the　efficient　selection　of　edge　nodes　and　the　optimization　of　resource　allocation,　especially　in　dynamic　and　resource-constrained　settings.　To　address　this,　we　propose　a　novel　architecture　called　Multi-Edge　Clustered　and　Edge　AI　Heterogeneous　Federated　Learning　(MEC-AI　HetFL),　which　leverages　multi-edge　clustering　and　AI-driven　node　communication.　This　architecture　enables　edge　AI　nodes　to　collaborate,　dynamically　selecting　significant　nodes　and　optimizing　global　learning　tasks　with　low　complexity.　Compared　to　existing　solutions　like　EdgeFed,　FedSA,　FedMP,　and　H-DDPG,　MEC-AI　HetFL　improves　resource　allocation,　quality　score,　and　learning　accuracy,　offering　up　to　5　times　better　performance　in　heterogeneous　and　distributed　environments.　The　solution　is　validated　through　simulations　and　network　traffic　tests,　demonstrating　its　ability　to　address　the　key　challenges　in　IoT　edge　computing　deployments.　©　The　Author(s)　2024.