The advent of quantum algorithms that involve the use of industrially relevant, resource intensive combinational logic operations, necessitates efficient optimization and transpilation of corresponding quantum circuits. This work proposes a multi-algorithm hybrid strategy to synthesize optimized quantum circuits from Boolean functions in the IBM native gate sets. The multilevel logic network representation, Xor-And-Inverter Graphs (XAG) networks, used to define Boolean functions, are optimized using a series of distinct XAG network optimization algorithms, which are applied in succession. This strategy assesses multiple solutions in the search space before efficiently converging to a stable set of native quantum gates, such as rz, sx, ecr, and x in the case of IBM-Brisbane backend. The main goal of this strategy is to reduce the depth, the number of AND and XOR gates in XAG networks and the overall cost and noise (resulting from lesser gates) associated with implementing native quantum gates in the quantum hardware. By utilizing advanced minimization strategies like re-substitution algorithms, this work further attempts to enhance quantum compilation efficiency. Finally, the proposed hybrid optimization strategy is evaluated and resource estimates of standard boolean functions with 6-inputs are presented. This strategy achieves an average decrease of 30.4% in the number of native quantum gates transpiled from quantum circuits corresponding to the final optimized XAG networks. This decrease was noted in comparison to the numbers transpiled from quantum circuits that were derived from respective intermediate XAG networks. The most efficient XAG network and the corresponding QASM (Quantum Assembly Language) code for a particular boolean function is obtained using this strategy.