ABSTRACT The Elastic Optical Network (EON) is a kind of optical network where a large amount of data signals are given into the channel of optical fibre by modulating with the optical signal. One of the most crucial tasks for optical networks is resource allocation and routing, which has a significant impact on resource effectiveness and network capacity. Deep learning-based routing and resource allocation have piqued the interest of researchers to a considerable extent. Thus, this paper aims to develop efficient link failure detection in EONs with hybrid deep learning strategies. Initially, the data aggregation is performed to combine the multiple link information from EONs. The aggregated link data is given into the hybrid detection model named as Enhanced Deep Learning Network (EDLNet), where the One-Dimensional Convolutional Neural Network (1D-CNN) is used for extracting the features and used for generating the target-based feature pool. This feature pool is utilised in the Long Short-Term Memory (LSTM) network for detecting link failures. The parameter optimization is done by the hybrid 1DCNN-LSTM network with the support of the Hybrid Red Deer with Sewing Training-based Optimisation (HRD-STO) to achieve accurate detection results. The detected failed link is mitigated at the time of routing, and only reliable links are used to attain certain objective constraints. These objective constraints include shortest path, link reliability and stability, throughput and delay for enhancing the efficiency of the EONs. The replication analysis reveals the effectiveness of the proposed link failure detection model by comparing it with the conventional models.