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Cooperative Adaptive Cruise Control (CACC) utilizes Vehicle-to-Vehicle (V2V) communications and onboard sensors to facilitate cooperative maneuvering among a group of automated vehicles called a vehicle string. Such string formation of automated vehicles enables improved safety, fuel efficiency, traffic flow, and road capacity. A vehicle using CACC computes its acceleration through information obtained from its preceding vehicle and/or the Leading Vehicle (LV) of the string through V2V communications. However, wireless communication is susceptible to inevitable transient outages due to irregular packet losses, which has severe consequences on the safety and stability of a vehicle string. To address this problem, this paper proposes an enhancement to an existing CACC algorithm; the idea is that when a vehicle does not receive information from its intended sources, i.e., the LV and the predecessor, for a certain duration, it uses information from the closest available longitudinal neighbors to the intended sources to compute its desired acceleration. Furthermore, we also investigate the possibility of using such information for training Machine Learning (ML) models and making predictions on the desired accelerations of the intended sources. Rigorous simulation studies demonstrate that when information from alternative sources is utilized during transient outages, a significant improvement in terms of safety, string stability, and fuel efficiency can be observed compared to the existing CACC. Moreover, the proposed approach can handle transient outages without requiring changes in the CACC communication topology, increasing the number of transmitted messages, or degrading string performance, as proposed by many works in the literature.