It is challenging to construct metrology schemes which harness quantum features such as entanglement and coherence to surpass the standard quantum limit. We propose an ansatz for devising adaptive-feedback quantum metrology (AFQM) strategy which reduces greatly the searching space. Combined with the Markovian feedback assumption, the computational complexity for designing AFQM would decrease from $N^7$ to $N^4$ , for N probing systems. The feedback scheme devising via machine learning such as particle-swarm optimization and derivative evolution requires much less time and produces equally well imprecision scaling. We have thus devised an AFQM for 207-partite system. The imprecision scaling would persist steadily for N > 207 when the parameter settings for 207-partite system is employed without further training.