This paper presents learning-enabled barrier-certified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact. That is, safety constraints are imposed by not only the ego system's own physical limitations but also other systems operating nearby. Since the model of the external agent is required to impose control barrier functions (CBFs) as safety constraints, a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents. More specifically, the loss function is defined based on barrier function error, instead of the system model error, and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set. The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF (L-ZCBF), which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations. It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents, which is crucial in safety-critical applications in highly interactive environments. The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area.