Efficient audio compression with low computational complexity is essential for real-time embedded systems operating under tough latency, memory, and hardware resource constraints. This paper presents a low-complexity hybrid audio compression framework that integrates Linear Predictive Coding (LPC), discrete wavelet transform (DWT) and Golomb entropy coding into a unified pipeline-oriented architecture for real-time FPGA implementation. The proposed framework uses LPC for short-term spectral modeling and residual extraction, Daubechies 4 wavelet transform for multi-resolution energy compaction and Golomb entropy coding for efficient compression of the resulting coefficients. The entropy coding scheme is lightweight and agreeable to hardware implementation. The architecture uses fixed-point arithmetic and pipelined processing to provide deterministic execution with low computational complexity on an Artix-7 FPGA platform. Experimental evaluation was performed on a 16 kHz uncompressed speech signal with 20 ms frames. The proposed framework achieved a compression ratio of 5.51 which is higher than that of LPC only (2.00), wavelet only (2.00) and MP3 (5.33) under the same evaluation conditions. The hardware implementation only used 3.83% LUT utilization, 0.94% flip-flop utilization and one DSP block. The processing latency of 0.037 ms per frame is significantly less than the 20 ms frame duration for real-time operation. Objective evaluation yielded SNR of 69.37 dB, STOI of 0.990, and PESQ of 2.19. This demonstrates that the suggested framework favors compression efficiency and hardware simplicity at the cost of reasonable reconstruction quality. The proposed LPC-Wavelet-Golomb architecture provides a practical compromise between compression performance, implementation complexity and real-time FPGA suitability for embedded audio compression applications.