Neuromorphic computing is a paradigm based on the computational mechanisms of the human brain and has received considerable attention as a real-time technique with low energy requirements. Present systems, however, are limited in their ability to scale traditional processors to a neuromorphic architecture, leading to issues with latency, power consumption, and smooth data flow. To address these problems, this paper proposes the ACORISC-VbSNN framework, comprising a modular RISC-V architecture, Spiking Neural Networks (SNNs), and Ant Colony Optimization (ACO). The system uses a shared-memory architecture to maximize communication between traditional and neuromorphic processors, ensuring data is managed effectively. The postulated framework processes the sensory data by pre-processing and encoding them using rate coding, and dynamically optimizing memory access. SNNs are also used to process spike trains in real-time, whereas ACO is used to determine the best data paths to minimize bottlenecks. Experimental analysis shows that the system performs better, with ultra-low power consumption of 0.0095 mW, very low latency of 0.000544 seconds, and 99.2 percent accuracy. These findings indicate that the ACORISC-VbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energy-efficient, and low-latency system for real-world use.