The K-Traveling Repairman Problem (K-TRP) is a challenging NP-hard optimization problem focused on minimizing total customer waiting time. Existing solution methods, however, frequently lack the adaptive mechanisms and sophisticated AI integration necessary to achieve state-of-the-art performance. This paper introduces Co-MARL-ATS, a hybrid solving framework founded on four synergistic pillars: a Multi-Agent System (MAS), Adaptive Tabu Search (ATS), Cooperative Reinforcement Learning (CRL), and an intelligent shared memory. A population of autonomous agents explores the search space in parallel, each guided by a DQN policy that controls six high-level actions: adjusting tabu tenure (increase/decrease), choosing intra- or inter-route operators, triggering diversification, and integrating shared solution patterns. Agents share solution fragments (patterns) via a smart repository that enables collective learning. Each agent’s policy learns not only how to search but also when to leverage the population’s discoveries. Rigorous computational experiments demonstrate clear superiority over all ablated versions (non-adaptive, non-cooperative, single-agent). Compared to state-of-the-art heuristics, Co-MARL-ATS establishes 8 new Best Known Solutions on large-scale instances, achieving an average Gap of -2.02% while maintaining runtimes competitive with existing methods (e.g., 3.20 s vs. QPSO 3.17 s). This synergistic design emphasizes the significant value of integrating adaptive learning, parallel search mechanisms, and coordinated collaboration to effectively address challenging combinatorial optimization problems.