Malware continues to pose a significant threat to cybersecurity, evolving rapidly in complexity and evasion techniques. Traditional detection methods often struggle against sophisticated attacks due to their reliance on static signatures or limited understanding of program behavior. This article introduces a novel dynamic malware detection approach that leverages both function parameter encoding and function dependency modeling derived from Application Programming Interface (API) call sequences. By capturing the rich contextual information conveyed through API call parameters and understanding the intricate relationships between function invocations, our method aims to provide a more robust and accurate classification of malicious software. We detail the methodology, from dynamic analysis and data collection to the feature engineering and model training, and present results demonstrating superior performance compared to existing techniques that primarily rely on API call sequences alone. The findings underscore the importance of deeper behavioral analysis for effective malware detection in the contemporary threat landscape.