Android malware has become a significant threat to mobile devices due to its ability to compromise user privacy, steal sensitive information, and cause financial damage. Traditional static analysis techniques have proven insufficient in detecting and classifying Android malware due to its evolving nature and the use of obfuscation techniques. This paper presents a novel dynamic analysis approach for the behavioral profiling of Android malware. The proposed approach involves executing the malware in a controlled environment and monitoring its behavior using predefined features. These features capture the malware’s interactions with the operating system, network, and other applications. Machine learning algorithms are then employed to classify the malware based on its behavioral profile. The proposed approach was evaluated using a dataset of hundreds of Android malware samples and demonstrates its effectiveness in detecting and classifying Android malware. Security researchers and practitioners can use the proposed approach to develop more effective defenses against Android malware.