Anxiety is a mental disorder that leads to palpitation, chest pain, behavioral abnormalities, etc. There are 301 million sufferers of anxiety disorders worldwide. Anxiety must be treated properly thus anxiety screening should be done more efficiently. EEG can detect abnormalities in the brain caused due to anxiety disorder. So, it can be used for screening anxiety. It may be able to overcome the drawbacks of conventional screening techniques. A machine learning-based method for screening anxiety among university-going students using wireless EEG is proposed in this study. EEG was recorded using a wireless 14-channel EEG headset from 40 students aged between 18-25 years. After using GAD-7 for screening the participants the EEG data of the participants was divided into anxiety group and anxiety control group. The data was filtered into 6 frequency bands. After extracting some nonlinear features, the SVM classifier with 10-fold cross-validation was used. Among all the bands the beta band (12-30Hz) had the highest accuracy of 94.88% with the Precision of 94.4%, NPV of 95.6%, Sensitivity of 97.2%, Specificity of 94.1%, and 0.96 F1 score.