An Android operating system is a comprehensive software framework for mobile communication devices (Smartphones, Tablet PC, watches, home appliances, In-Vehicle Infotainment systems). End users or consumers are attracted by various interesting features offered by these devices and the associated applications. However, Android devices are still vulnerable to several types of attacks, a particularly concerning one being privacy leakage. Apart from providing several user-experience features, these systems store and share more sensitive data throughout the day. The sensitive information includes not only the personal data of the user but also the data collected through the vehicle sensors. The breach/leakage of the sensitive data may impact the vehicle manufacturer because of the stringent privacy regulation requirements. Thus, the vehicle manufacturer needs to adopt a privacy leakage detection mechanism to identify any potential source of leakage and mitigate it. There are a lot of privacy leakage detection methods available today, and the main task for an organization is to determine which one to use considering its effectiveness and inclusion with their requirements. However, there is a lack of review study of existing methods, tools, and good practices to detect privacy leakage in the Android system. This review study aims to provide an overview of the current state of the methods and tools for privacy leakage detection in the Android system. Firstly, we performed a comparative study to analyze the execution and effectiveness of various existing privacy monitoring techniques/ schemes. Subsequently, an evaluative study is performed through a use case of the personal data involved in automotive In-Vehicle Infotainment (IVI). We identified various data components present in IVI and classified them based on different sensitivity levels and their transmission outside the IVI along with the associated scenarios. Furthermore, we evaluated the privacy issues with the data transmission through the privacy leakage detection tools. Finally, we summarized which privacy leakage detection techniques are more effective for IVI.