Aiming at an automated method for forensic audio analysis, the Advanced Deep-Learning Framework for Comprehensive Audio Forensics: Classification and Frequency Analysis searches Leveraging deep learning techniques—more especially, combining a hybrid model with Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) layers—enhanced by attention mechanisms—the proposed framework classifies and evaluates several audio types. Essential for forensic investigations, the architecture is designed to discover abnormalities, split frequency ranges, and manage several audio sources. CNNs derive features from preprocessing and frequency segmentation. For sequential analysis, bi-LSTM layers inherit these properties, therefore ensuring a robust understanding of temporal trends. The attention mechanism helps to enhance the model even further sharpens the model's focus on relevant frequency bands for exact classification and anomaly identification. In many environmental contexts, our deep learning architecture enables the recognition of significant acoustic characteristics. The tool can control several forensic audio sources, ranging in frequency from skating to sleeping to snoring. This provides law enforcement and forensic experts with a quick tool for objective audio analysis since it offers flexibility in practical forensic environments. Apart from classification, the framework consists in audio segmentation, frequency detection, and anomaly detection, so augmenting the overall forensic analysis of audio data.