Stochastic computing (SC) has emerged as an efficient paradigm for low-power, fault-tolerant processing in applications like neural networks and image processing. SC uses bitstream-based stochastic sequences to represent probabilities, making it inherently resilient to noise and computational errors. However, the accuracy of SC operations depends significantly on the quality of these sequences, typically generated by linear feedback shift registers (LFSRs). This study analyzes the impact of different LFSR architectures—Fibonacci, Galois, and Mixed—on SC accuracy. Experiments revealed that Fibonacci LFSR, with its series-connected XOR configuration, achieved the lowest error rate, followed by Mixed and then Galois LFSR, which showed a significantly higher error rate. Additionally, results indicate that increasing the length of the stochastic sequence generally reduces the error rate across all LFSR types, while smaller operation results are associated with higher error rates. These findings suggest that carefully selecting the LFSR design and sequence length is crucial for optimizing SC processor accuracy, particularly in delay-sensitive environments where Mixed LFSR offers a balanced trade-off between accuracy and hardware efficiency. Overall, this research provides essential insights into LFSR configuration strategies, supporting the development of SC processors tailored to energy-efficient, real-time applications, and guiding future advancements in SC architecture.