In the field of Brain–Computer Interface (BCI), identification of Motor Imagery (MI) EEG signals is a challenging task. People with neuromuscular impairments can use the MI-BCI system to perform their day-to-day activities on their own. In this work, MI-EEG signals are obtained from BCI Dataset Iva, and spatial features (CSP) are helpful to identify the distinct characteristics of MI signal. The motor imagery actions are further classified by Bidirectional LSTM (BiLSTM) Neural Network with maximum mean accuracy of 98.36%. An enhanced LSTM model called the BiLSTM’s ability to handle the complex temporal dynamics of EEG signals showcases the effectiveness of this robust approach. This BiLSTM with spatial features is compared with Random Forest, XGBoost, and LSTM models and outperforms by 4.7%. This approach mainly focuses on minimizing the validation loss and contributes to a reduced misclassification rate. This research highlights the potential of deep learning algorithms by means of improved accuracy and reliability of BCI systems. These implications made a significant impact on the advancement of future BCI systems and could potentially lead to the development of more sophisticated assistive devices for motor impaired patients.