Aim: To compare and study the novel AdaBoost algorithm (NABA) and Random Forest algorithm for text wise toxic speech prediction for the purpose of enhanced accuracy of real-time voice detection. Materials and Methods: The novel AdaBoost algorithm (N= 10) and Random Forest algorithm (N=10) methods are simulated by varying the NABA and random forest parameters to increase the pH. With the help of Gpower (80%) for two groups, the sample size is calculated as 20 samples per group for text analysis. Results and Discussion: Based on obtained results NABA has significantly better accuracy (95.69%) compared to Random forest accuracy (80.33%). The statistical significance difference between AdaBoost and Random Forest was found to be p=0.129 (p<0.05) independent sample T-test value states that the groups are statistically insignificant. Conclusion: AdaBoost algorithm produces better results in predicting toxic speech to improve accuracy percentage than the random forest algorithm.