A large number of studies including research articles and surveys on android malware detection and analysis techniques have been presented during the last one and a half decades. The authors proposed different systems and frameworks to identify malware from software applications. However, there is no recent and comprehensive systematic literature review on the detection and analysis of android malware methods, systems, and frameworks. We present a systematic review of literature on android malware detection and analysis techniques and tools by following standard guidelines for Systematic Literature Review methodology from 2010 to 2021. We selected 75 most relevant studies out of 3343 published studies. We found that the prominent malicious datasets are Genome (39%) and Drebin (36%) used by different researchers for the detection of malware. The static, dynamic, and hybrid source code analysis methods are applied by android malware detection techniques. We also identified the limitations and future research directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis-based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic-based approaches, but it also analyzes evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy of evolving malware samples.