AnoLNN is a novel, generalisable, and explainable framework for Video Anomaly Detection, designed to address key challenges in the field such as limited generalisability, high computational costs, data imbalance, and lack of interpretability. The system integrates a hybrid architecture that combines Convolutional Neural Networks for spatial feature extraction with Liquid Time-Constant Networks for capturing dynamic temporal dependencies. The model learns from overlapping video sequences and employs a class-balanced focal loss function to handle the natural class imbalance common in VAD datasets. Anomalies are detected by evaluating the confidence of sequence-level predictions, using F1 score-based threshold optimisation. To enhance transparency and trust, the system integrates Grad-CAM and Integrated Gradients, enabling the identification of critical spatial and temporal features contributing to anomaly predictions. Evaluated on the UCF-Crime dataset, AnoLNN achieved strong performance with an AU-ROC of 0.9787 and PR-AUC of 0.9568 for Road Accidents, as well as an average AU-ROC of 0.9090 across multiple anomaly types. These results highlight its state-of-the-art performance and adaptability across diverse scenarios. The explainability features built into the system support its safe deployment in real-world applications such as surveillance and autonomous vehicles.