Central Pattern Generators (CPGs) are neural circuits capable of autonomously producing rhythmic output patterns without requiring rhythmic input. They maintain stable phase relationships among constituent oscillators, adapt to sensory feedback and descending modulation, and exhibit resilience to perturbations. This work presents a novel hybrid Analog-Digital CPG architecture that leverages the continuous, low-latency dynamics of analog oscillators alongside the flexibility, reconfigurability, and precision of digital control. The proposed system features tunable parameters and a modular design, enabling real-time adaptation to sensory inputs and environmental conditions. This approach provides a versatile framework for generating robust, adaptable gait patterns, with applications spanning autonomous robotics, soft robotics, and human-assistive technologies.