Abstract: We present the N-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino events (similar to 100 GeV). N-Fit is a neural network model that relies on deep learning and combines several advanced techniques in machine learning-deep convolutional layers, mixture density output layers, and transfer learning (TL). This framework divides the reconstruction process into two dedicated branches for each neutrino event topology-tracks and showers-composed of sub-models for spatial estimation-direction and position-and energy inference, which later on are combined for event classification. Regarding the direction of single-line (SL) events, the N-Fit algorithm significantly refines the estimation of the zenithal angle, and delivers reliable azimuthal angle predictions that were previously unattainable with traditional chi 2-fit methods. Improving on energy estimation of SL events is a tall order; N-Fit benefits from TL to efficiently integrate key characteristics, such as the estimation of the closest distance from the event to the detector. N-Fit also takes advantage from TL in event topology classification by freezing convolutional layers of the pretrained branches. Tests on Monte Carlo simulations and data demonstrate a significant reduction in mean and median absolute errors across all reconstructed parameters. The improvements achieved by N-Fit highlight its potential for advancing multimessenger astrophysics and enhancing our ability to probe fundamental physics beyond the Standard Model using SL events from ANTARES data.