The high penetration of distributed generation, inverter-interfaced renewable energy sources, and dynamic microgrid operation has drastically transformed short-circuit behavior, leading to frequent miscoordination of conventional protection schemes. Bidirectional fault currents, variable fault levels, and network reconfiguration further complicate the coordination of directional overcurrent relays (DOCRs). To overcome these challenges, this paper proposes a deep learning–enabled hybrid Artificial Bee Colony (ABC) and NSGA-II–based multi-objective optimal protection coordination (DL–ABC–NSGA-II MO-OPC) framework for renewable-integrated power systems. The protection coordination problem is formulated as a constrained multi-objective optimization model, aiming to: (i) minimize the total operating time of primary and backup relays, (ii) maximize coordination margins under coordination time interval (CTI) constraints, and (iii) enhance protection security under bidirectional inverter-dominated fault currents and multiple network topologies. The decision variables include time multiplier settings (TMSs), plug setting currents (PSCs), and relay curve characteristics. A deep learning model is embedded within the ABC search process to predict promising regions of the search space, accelerate convergence, and adaptively tune control parameters under varying fault and loading conditions. The refined solutions are then evolved using NSGA-II to generate a well-distributed Pareto-optimal front. The proposed DL–ABC–NSGA-II framework is validated on a modified benchmark power network under grid-connected and islanded modes, considering multiple fault types and renewable penetration scenarios. Simulation results confirm a substantial reduction in overall relay operating time, complete elimination of miscoordination, and strong robustness against renewable-induced fault current uncertainty. Comparative analysis with conventional coordination and recent metaheuristic-based approaches demonstrates the superior convergence speed, enhanced solution diversity, and improved protection reliability of the proposed scheme. The proposed deep learning–assisted protection coordination strategy provides a scalable, intelligent, and cyber-resilient solution for next-generation smart grids and microgrids with high renewable energy penetration.