Our proposal introduces a novel application of Matsuoka's Central Pattern Generator (CPG) algorithm to bio-inspired robotics, with an emphasis on aquatic locomotion. By adapting the algorithm based onMatsuoka's model, we enable its translation from continuous to discrete form, ensuring its compatibility with digital processors for realtime robotic control. The method demonstrates a remarkable improvement, exhibiting a performance rate approximately 50% higher than that achieved with traditional algorithms such as Pulse Width Modulation (PWM). The refinement of the control algorithm within bio-inspired robotic systems allows for a replication of fish-like movements more closely aligned with natural behaviors. This paper delves into the CPG algorithm's architecture, which has been modified to address discrete processing requirements without sacrificing the fluidity of biomimetic motion. We present empirical evidence showing our algorithm's superiority to conventional control strategies by examining the robot's enhanced capability to mimic accurate and efficient swimming patterns. Notably, the application of this algorithm reduces temporal errors and delivers a marked enhancement in underwater robotic performance. Our findings indicate a promising direction for future research and application in the field of robotic swimming, suggesting a paradigm shift in the approach to robotic movement inspired by biological systems.