Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off between precision and complexity for conventional methods. To address this, we propose a novel gridless sparse Bayesian learning (SBL) framework for joint super-resolution multi-target detection and high-accuracy parameter estimation with manageable computational cost. Our model treats target parameters as continuous variables to bypass the grid limitations of conventional approaches. This SBL formulation, however, transforms the estimation task into a challenging high-dimensional inference problem, which we address by developing an enhanced gradient-based Markov chain Monte Carlo algorithm. Our method integrates mini-batch sampling and the Adam optimizer to ensure computational efficiency and rapid convergence. Finally, we validate the framework's robustness in strong clutter and provide a theoretical benchmark by deriving the corresponding Bayesian Cramer-Rao bound. Simulation results demonstrate remarkable super-resolution capabilities, successfully resolving multiple targets separated by merely 50% of the Rayleigh limit in range, 17% in velocity, and 52% in angle. At a signal-to-noise ratio of 20 dB, the algorithm achieves a multi-target detection probability exceeding 90% while concurrently delivering ultra-high accuracy, with root mean square error of 0.07 m, 0.024 m/s, and 0.015 degree for range, velocity, and angle, respectively. This robust performance, demonstrated against strong clutter, showcases its suitability for practical ISAC-LAWNs applications.