Aim: To Improve accuracy of weather prediction in dense forest using Naive Bayes and Convolutional Neural Network Materials and Methods: weather prediction performed using Naive Bayes (N=10) and Convolutional Neural Network (N=10) with the split size of training and testing dataset 60% and 40% using G-power setting parameters: (α=0.05 and power=0.85) respectively Results: Naive Bayes With Accuracy 84% is more Accurate than the Convolutional Neural Network with Accuracy 80% and attained the significance value 0.0176 (Two tailed, p>0.05) Conclusion: The Naive Bayes model is significantly better than the Convolutional Neural Network for Weather Prediction in Dense Forest.