The complexity of combustion phenomena caused by the interaction between chemical kinetics and turbulence has been investigated in this thesis using Laminar Flamelet model along with the Artificial Neural Networks technique with the least possible simplifications. The application of Flamelet model provides the possibility of the usage of every complicated chemical mechanism. However, in most of other models because of the simultaneous solution of kinetics and turbulence, researchers have to implement reduced mechanisms to prevent the increase in requisite CPU time and memory resulting in more discrepancies. Another point in this work is the application of A as a shortcut between the solutions of chemical kinetics and turbulent fluid flow. This has yielded a coicuous reduction in the time-consumption of solution without any defect on the preciseness of results. In this work, first of all, a data bank is built using the solution of opposed diffusion flames in different strain rates. Then in that data bank, species mass fractions and temperatures are related to mixture fractions and scalar dissipation rates which are calculated from the information existing in the aforementioned data bank. In the next step, for the consideration of turbulence effects, numerical integration has been done using probability density functions (PDF). As a result of numerical integration, the favre average amounts of thermo-chemical properties of flow in various amounts of mean mixture fractions, mixture fraction variances and mean scalar dissipation rates can be achieved. Now a Flamelet library has been built over which an ANN can be built. Weights and biases of the already constructed ANN have been implemented in the CFD code to predict the values of species mass fractions and temperature in different locations of the solution domain. Consequently, in the CFD code there's no need for the solution of every single species traortation equation. That's why a considerable reduction in CPU time has been observed. It's noteworthy that important minor species including some pollutants has been analyzed as well and an acceptable agreement has been observed comparing with experimental results. Moreover, the effect of different probability density functions has been investigated in the numerical integration. It has been perceived that the application of Log-normal PDF as a function of scalar dissipation rate yields better agreement in comparison with the usage of Delta PDF as a function of scalar dissipation rate. However, those slight differences between already mentioned results can be neglected because of more time-consumption in the former state. Key Words: Non-Premixed Combustion, Artificial Neural Network, Laminar Flamelet Model, Turbulence, Chemical Kinetics, Probability Density Function.