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The gearbox is one of the critical components of a wind turbine. Proactive maintenance of wind turbine gearboxes is crucial to decrease maintenance and operational costs and the long downtime of the complete system. As the gearbox is a significant part of the wind turbine, a fault in the gearbox leads to the breakdown of the wind turbine system. Hence, it is important to study and analyze the faults in wind turbine gearbox systems. In this article, a neural network-based model, a Bidirectional Long Short-Term Memory (BLSTM) fused with an autoencoder is intended to categorize the condition of the gearbox into good or bad (broken tooth) condition. Feature learning and reduction are achieved extensively through the autoencoder. This improves the performance of the BLSTM model regarding time complexity and classification accuracy. This model has been applied with time series vibration data of the gearbox in a wind turbine system. The suggested model's performance is analyzed using an openly available wind turbine gearbox vibration dataset. The result showed that BLSTM accuracy with an under-complete autoencoder is highly robust and appropriate for the health monitoring of wind turbine gearbox systems using time series data. Also, in order to illustrate the advantage of the projected model for fault analysis and diagnosis in wind turbine gearbox, the throughput or time complexity of training and testing of the split dataset is compared with the conventional BLSTM model.