Speech coding is a widely used technique used for digital telephony and secure communication. The estimation of linear prediction coefficients (LPCs) for the development of synthetic speech is crucial and involves the use of the Levinson–Durbin (LD) algorithm. The computational complexity introduced by this algorithm affects the performance of speech codecs utilizing LPCs. In this paper, an FPGA implementation of the Levinson–Durbin algorithm is proposed for efficient autoregressive model parameter estimation. By harnessing FPGA’s parallel processing capabilities, the algorithm’s performance is accelerated, allowing real-time processing of signals. The work focuses on translating the algorithmic steps into hardware modules, optimizing memory access, and evaluating resource utilization, and power efficiency of the implemented hardware. This research contributes to the field of hardware-accelerated algorithms, showcasing the potential of FPGA platforms in enhancing signal processing tasks.