Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis. In this study, we investigated the parameters for predicting participants’ severity level of Cynophobia and Astraphobia by using the following measures: “APA Specific Phobia Severity Measure - Adult”, “Distance and Time”, “Heart Rate and Oxygen levels for each level” in VR-specific-phobia diagnostic environment, “symptoms” observed during experimentation, and “causes” described by DSM-5. The “APA Specific Phobia Severity Measure - Adult” is attributed as the standard used by psychiatrists for clinical evaluation. We used the score of this measure to classify instances for each participant. The other parameters serve as attributes for predicting class, implementing the process of Data Mining. The literature supports the prior mentioned parameters for assessing severity levels for specific phobia. The participant walks or runs along a road in a Virtual Reality Environment to achieve the objective. The first scenario is a neutral environment with no phobic stimulus; the afterward situations pose for a dog cue, thunder lightning stimulus, and a combination of both stimulation consecutively. The ‘Distance’ traveled and ‘Time’ taken in units for each VR scenario generated using a Bluetooth controller is saved in a file with time stamps. The participant subsequently fills Google Form to record the parameters. The dataset is converted to ARFF format, and the process of Knowledge Discovery is applied using the WEKA tool. The results suggest that the presence of Cynophobia and Astraphobia are highly interrelated. The study advised that Dog-Phobia severity level confidently predicts with the parameters “Age”, “Time” in Neutral scenario, “Distance” covered in Cynophobic scenario“, ”Difference in Oxygen levels” of Cynophobic VRE and scenario with both (Dog and Thunder Lightning) stimuli and “DSMAstraphobia”. The research analysis concludes that thunder-lightning phobia severity level effectively forecasts with these attributes: “Velocity”, “Distance” and “Time” in Neutral VRE scenario“; ”Velocity“, ”Time” VRE scenario for both pre-mentioned phobic stimuli; “Time” in Cynophobic scenario, “Velocity” calculated in Astraphobic VRE, “Age” of the participant and DSMCynophobia. This study will help in suggesting standards for diagnosing mental health problems with the advantages of VR.