This paper proposes a novel framework for deep learning-based anomaly detection in videos using the Squirrel Search Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM). The framework combines the SSA, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The SSA is used to optimize the search space for flying squirrels, while BiLSTM is used to process video sequences and classify them as normal or abnormal. The proposed method was tested on several benchmark datasets and achieved an area under the curve (AUC) of 93.1% on the test set, outperforming existing methods. The study also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional LSTM models for video anomaly detection. The framework provides a highly accurate system for identifying abnormal human behavior in surveillance footage. The SSA is based on the behavior of flying squirrels in deciduous forests, where they glide between trees to find food. The algorithm uses a seasonal monitoring condition to improve search space exploration. The BiLSTM model is used to process video sequences and classify them as normal or abnormal. The study also compares the proposed method with other existing techniques and shows that it achieves better performance in terms of accuracy, precision, recall, and F1 measure. The results demonstrate that the proposed framework is effective for identifying abnormal human behavior in surveillance footage.This paper proposes a novel framework for deep learning-based anomaly detection in videos using the Squirrel Search Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM). The framework combines the SSA, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The SSA is used to optimize the search space for flying squirrels, while BiLSTM is used to process video sequences and classify them as normal or abnormal. The proposed method was tested on several benchmark datasets and achieved an area under the curve (AUC) of 93.1% on the test set, outperforming existing methods. The study also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional LSTM models for video anomaly detection. The framework provides a highly accurate system for identifying abnormal human behavior in surveillance footage. The SSA is based on the behavior of flying squirrels in deciduous forests, where they glide between trees to find food. The algorithm uses a seasonal monitoring condition to improve search space exploration. The BiLSTM model is used to process video sequences and classify them as normal or abnormal. The study also compares the proposed method with other existing techniques and shows that it achieves better performance in terms of accuracy, precision, recall, and F1 measure. The results demonstrate that the proposed framework is effective for identifying abnormal human behavior in surveillance footage.