Navigation in complex environments is a key challenge in robotics and autonomous systems, traditionally tackled with pathfinding algorithms like A \(^{*}\) and its variants. These methods discretise the domain into a uniform grid, enabling movement between adjacent nodes. While computationally efficient, these approaches compromise path smoothness and optimality due to the limited number of movement directions. Alternatively, Any-Angle path planning methods address this by using visibility graphs, allowing direct connections between nodes when there is no obstacle between them. This improves trajectory flexibility but significantly increases computational costs, limiting scalability. To overcome these challenges, this paper introduces a Deep Learning-accelerated approach for efficient navigation in obstacle maps. A customised Conditioned U-Net, trained on a solution database, provides heuristic estimates to guide the search. Combined with a k-Non Aligned Nearest Neighbours (k-NANN) graph structure, this method ensures smooth, optimal trajectories while reducing computational overhead.