Malware is the umbrella term that denotes attacking any system by malicious software. During the last few years, the popularity of Android smartphones led to the sneak of several malware applications into different Android markets without any difficulty. As a consequence of this, malware applications have been grown exponentially in the Android markets. Unfortunately, most of these markets suffer from an inability to detect malware, which results in increasing the probability of infecting users’ phones with these malware applications. The present paper focuses on developing an efficient computational framework based on Deep Belief Networks for malware detection. The proposed framework merges high level static analysis, dynamic analysis and system calls in feature extraction in order to achieve the highest accuracy. The evaluation compares the most familiar machine learning approaches that were applied in malware detection with the proposed framework. The obtained results demonstrate that Deep Belief Networks technique can realize 99.1% accuracy with the presented dataset. Over and above that, we develop our complete static analysis jar which adopts different efficient methods in an attempt to facilitate and speed up the static analysis by handling all the Android applications in only one step rather than considering one application at a time. Keywords: Android, Static analysis, Malware detection, Dynamic analysis, System calls, Deep Belief Networks, Deep learning