In the field of logistics, the continuity of operations is secured through proative management of equipment, where preventive maintenance could predict future failures, reduce downtime and overall enhance supply chain efficacy. The present study focuses on the application of artificial intelligence techniques to achieve accurate predictions and, thus, proactive maintenance planning. Accordingly, a comparative analysis of the recurrent neural network and convolutional neural network methodologies was coducted on open-source data to identify the superior approach in various operational scenarios. Critical indicators were used as data such as temperature and vibrations among other performance measures. Thus, it is preprocessed and further analyzed with the aim of a real-time status prediction of equipment. The obtained results suggest that the use of AI and neural networks could alleviate downtime, optimize the costs spent on maintenance, and increase the reliability of logistic systems. In the general view, the present study reveals a very promising avenue for further developments in the domain in quetion.