This　article　explores　detecting　and　categorizing　network　traffic　data　using　machinelearning　(ML)　methods,　specifically　focusing　on　the　Domain　Name　Server　(DNS)　protocol.　DNS　has　long　been　susceptible　to　various　security　flaws,　frequently　exploited　over　time,　making　DNS　abuse　a　major　concern　in　cybersecurity.　Despite　advanced　attack,　tactics　employed　by　attackers　to　steal　data　in　real-time,　ensuring　security　and　privacy　for　DNS　queries　and　answers　remains　challenging.　The　evolving　landscape　of　internet　services　has　allowed　attackers　to　launch　cyber-attacks　on　computer　networks.　However,　implementing　Secure　Socket　Layer　(SSL)-encrypted　Hyper　Text　Transfer　Protocol　(HTTP)　transmission,　known　as　HTTPS,　has　significantly　reduced　DNS-based　assaults.　To　further　enhance　security　and　mitigate　threats　like　man-in-the-middle　attacks,　the　security　community　has　developed　the　concept　of　DNS　over　HTTPS　(DoH).　DoH　aims　to　combat　the　eavesdropping　and　tampering　of　DNS　data　during　communication.　This　study　employs　a　ML-based　classification　approach　on　a　dataset　for　traffic　analysis.　The　AdaBoost　model　effectively　classified　Malicious　and　Non-DoH　traffic,　with　accuracies　of　75%　and　73%　for　DoH　traffic.　The　support　vector　classification　model　with　a　Radial　Basis　Function　(SVC-RBF)　achieved　a　76%　accuracy　in　classifying　between　malicious　and　non-DoH　traffic.　The　quadratic　discriminant　analysis　(QDA)　model　achieved　99%　accuracy　in　classifying　malicious　traffic　and　98%　in　classifying　non-DoH　traffic.