We present a novel framework that combines machine learning with mixed-integer optimization to solve the Capacitated Location-Routing Problem (CLRP). The CLRP is a classical yet NP-hard problem that integrates strategic facility location with operational vehicle routing decisions, aiming to simultaneously minimize both fixed and variable costs. The proposed method first trains a permutationally invariant neural network that approximates the vehicle routing cost for serving any arbitrary subset of customers by any candidate facility. The trained neural network is then used as a surrogate within a mixed-integer optimization problem, which is reformulated and solved using off-the-shelf solvers. The framework is simple, scalable, and requires no routing-specific knowledge or parameter tuning. Computational experiments on large-scale benchmark instances confirm the effectiveness of our approach. Using only 10,000 training samples generated by an off-the-shelf vehicle routing heuristic and a one-time training cost of approximately 2 wall-clock hours, the method provides location-allocation decisions that are within 1 % of the best-known solutions for large problems in less than 5 seconds on average. The findings suggest that the neural-embedded framework can be a viable method for tackling integrated location and routing problems at scale. Our code and data are publicly available.