This paper presents the role of neuro-fuzzy logic in real world problems. An Artificial Neural network will process the data set using different algorithms like Perceptron, Adaline, and back propagation algorithms such that the weight should be trained for better performance. To do this, different types of activation functions are required to meet the optimum result of the data set. Due to adaptable characteristic of neural network; it can adjust their weights for new data set predictors through learning phase. With respect from fuzzy logic, which produces uncertain values that can be mapped from zero to one along with all the intermediate values that lies in-between them through fuzzy sets. So, neural network can work on maximum implementation with learning and training while fuzzy logic deals with imprecise information. But Neural Networks when combined with Fuzzy Logic generate intelligent systems that can resolve a number of complex problems in no time with less mean error. We can apply fuzzy rules with neural data set that can integrate to resolve specific problems. The concept of Adaptive Neuro Fuzzy Inference System was introduced under the concept of adaptability of bias and weight values of the neuron with the processing power of fuzzy inference system. Various types of real world problems and new directions might be achieved for neuro-fuzzy with multiple applications. The goal of this paper is to explain the role of neuro-fuzzy systems; and to implement one of the sample instances of weather prediction by using WEKA Tool. This tool clearly represents that multilayer perceptron algorithm that is common in neural networks when related with fuzzy logic would produce better results as prescribed in data set.