The stable integration of renewable energy sources into DC microgrids (DCMGs) is hindered by significant challenges in fault detection and control, often leading to system-wide instability. This study addresses these issues by introducing a novel, integrated framework. We propose a resistance-based fault identification strategy for swift fault detection and precise localization, effectively containing faults and mitigating the risk of cascading failures. To manage the resulting voltage-current (V-I) variations, this paper further develops a Proportional-Integral (PI) controller whose parameters are dynamically optimized using an Artificial Bee Colony (ABC) algorithm. The ABC algorithm is selected for its superior control capabilities in complex, nonlinear systems, ensuring operation within required limits for voltage, current, and power ripple. The applicability and correctness of the proposed methodologies were rigorously validated through extensive digital simulations, with performance benchmarked against unoptimized conditions. This research offers a significant advancement in DCMG technology by demonstrably increasing operational efficiency, enhancing dynamic stability, and improving overall control performance for future-oriented, resilient power systems.