Achieving the stringent requirements of ultra reliable low latency communication (URLLC) in vehicular networks, characterized by high mobility, presents significant challenges. Orthogonal time frequency space (OTFS) modulation has surfaced as a promising solution to tackle Doppler shifts and delay spreads in high-mobility wireless channels by mapping symbols into the delay-Doppler (DD) domain. This paper introduces a novel OTFS-enabled transceiver framework for predictive beamforming in vehicular networks. The framework employs frequency division duplex (FDD) to diminish latency and enhance flexibility, with the predictive beamforming technique implemented at the transmitter to improve the received signal strength (RSS). Owing to the latency constraints of URLLC, predictive beamforming necessitates leveraging historical channel state information (CSI) in the DD domain. In this context, a deep learning (DL) approach using the ConvLSTM network is harnessed for predictive beamforming, concentrating on capturing spatial-temporal correlations of high-mobility wireless channels from historical DD domain CSI. Extensive simulations validate the efficacy of our proposed DL-based predictive beamforming for OTFS-enabled URLLC in vehicular networks.