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• An advanced deep learning model is proposed to predict the quality of transmission (QoT) (Q-factor, chromatic dispersion) of a light-path. • The proposed model was tested on a data-set that contains data extracted from Microsoft's optical backbone network in North America. • The performance of the proposed model is compared with reference approaches viz; SVM, MLP, NN, and LSTM to show if such models could be considered for predicting the estimated received Q-factor and chromatic dispersion of established light-paths. • Results exhibit that the proposed model achieves the best performance across multiple channels of this data-set and even in a case when the size of the training data-set is small. Increasing traffic demand in the backbone optical network urges the initiation of new solutions that allow an increment in the transmission/network capacity without a considerable rise in service costs. To attain this objective, simpler and quicker network reconfiguration processes is required. A significant issue intrinsically correlated with network reconfiguration is the quality of transmission (QoT) (BER, SNR, Q-factor, chromatic dispersion and polarization mode dispersion) estimation. Predicting the QoT of already established light-paths enable network operators to respond proactively to performance deterioration, which helps in enhancing or sustaining quality of service (QoS). Recently, several deep learning-based approaches have been presented to estimate the QoT of an optical network. This paper proposes an advanced deep learning model comprising a long short-term memory (LSTM) network along with three fully connected layers that leverage the transmission quality metrics collected from the network's control plane to predict QoT of light-path. The three additional fully connected layers help the model learn the more complex patterns in the data that a basic LSTM cannot learn on its own and improve the model's performance by three orders of magnitude. Moreover, the proposed model doesn't require a large data-set to provide high accuracy results, which decreases the control plane's load. Our model was tested on a data-set that contains data extracted from Microsoft's optical backbone network in North America. We compare the performance of the proposed model with reference approaches viz; SVM, MLP, NN, and LSTM to show if such models could be considered for predicting the estimated received Q-factor and chromatic dispersion of established light-paths. Results exhibit that the proposed model achieves the best performance across multiple channels of this data-set and even in a case when the size of the training data-set is small. The proposed model provides high prediction accuracy, which is perfect for the next-generation agile optical network. [ABSTRACT FROM AUTHOR]