Android has dominated a significant portion of the cellphone market as one of the most developed intelligent operating systems for mobile devices. However, the issue of Android malware detection remains critical due to the security mechanism and the lack of stringent validation during the publishing of Android apps, leading to potential breaches of user privacy through unwanted permissions. As mobile app security and privacy remain critical, integrating the machine learning model into Android applications offers a practical solution. This paper proposes an Android application using machine learning models. The application provides a user-friendly interface to assess the permissions requested by various apps and recommends the appropriate actions based on the permissions’ safety ratings. The model focuses on the safety of app permissions based on their usage frequency in specific app categories available on the Google Play Store. Subsequently, the permissions are evaluated and rated as either safe or unsafe. This paper contributes to a safer mobile app ecosystem by providing users with a comprehensive tool for making informed app permissions decisions.