The Location-Routing Problem (LRP), which combines the challenges of facility (depot) locating and vehicle routing, is critically constrained by the reliance on predefined depot candidates, limiting the solution space and potentially leading to suboptimal outcomes. Existing research on LRP without predefined depots is scant and predominantly relies on heuristic algorithms that iteratively attempt depot placements across a planar area. Such approaches lack the ability to proactively generate depot locations that meet specific geographic requirements, revealing a notable gap in current research landscape. To address this gap, we propose a generative Deep Reinforcement Learning (DRL) framework that proactively generates depot locations directly from customer geographic and demand information. By extracting depots’ geographic pattern from customer requests, our approach can dynamically respond to logistical needs, identifying high-quality depot locations that further reduce total routing costs compared to traditional methods. Extensive experiments demonstrate that, for a same group of customer requests, compared with those depots identified through random attempts, our framework can proactively generate depots that lead to superior solution routes with lower routing cost. Its adaptability makes it particularly suitable for urgent real-world scenarios, such as emergency medical rescue and disaster-relief logistics, where rapid, dynamic depot establishment and adjustment is essential.