The　growing　usage　of　Android　smartphones　has　led　to　a　significant　rise　in　incidents　of　Android　malware　and　privacy　breaches.　This　escalating　security　concern　necessitates　the　development　of　advanced　technologies　capable　of　automatically　detecting　and　mitigating　malicious　activities　in　Android　applications　(apps).　Such　technologies　are　crucial　for　safeguarding　user　data　and　maintaining　the　integrity　of　mobile　devices　in　an　increasingly　digital　world.　Current　methods　employed　to　detect　sensitive　data　leaks　in　Android　apps　are　hampered　by　two　major　limitations　they　require　substantial　computational　resources　and　are　prone　to　a　high　frequency　of　false　positives.　This　means　that　while　attempting　to　identify　security　breaches,　these　methods　often　consume　considerable　processing　power　and　mistakenly　flag　benign　activities　as　malicious,　leading　to　inefficiencies　and　reduced　reliability　in　malware　detection.　The　proposed　approach　includes　a　data　preprocessing　step　that　removes　duplicate　samples,　manages　unbalanced　datasets,　corrects　inconsistencies,　and　imputes　missing　values　to　ensure　data　accuracy.　The　Minimax　method　is　then　used　to　normalize　numerical　data,　followed　by　feature　vector　extraction　using　the　Gain　ratio　and　Chi　-squared　test　to　identify　and　extract　the　most　significant　characteristics　using　an　appropriate　prediction　model.　This　study　focuses　on　extracting　a　subset　of　attributes　best　suited　for　the　task　and　recommending　a　predictive　model　based　on　domain　expert　opinion.　The　proposed　method　is　evaluated　using　Drebin　and　TUANDROMD　datasets　containing　15,036　and　4,464　benign　and　malicious　samples,　respectively.　The　empirical　result　shows　that　the　Random　Forest　(RF)　and　Support　Vector　Machine　(SVC)　classifiers　achieved　impressive　accuracy　rates　of　98.9%　and　98.8%,　respectively,　in　detecting　unknown　Android　malware.　A　sensitivity　analysis　experiment　was　also　carried　out　on　all　three　ML　-based　classifiers　based　on　MAE,　MSE,　R　2　,　and　sensitivity　parameters,　resulting　in　a　flawless　performance　for　both　datasets.　This　approach　has　substantial　potential　for　real　-world　applications　and　can　serve　as　a　valuable　tool　for　preventing　the　spread　of　Android　malware　and　enhancing　mobile　device　security.