Online advertisement has become a major commercial campaign in social networks. Many big companies have invested massive resources for collecting data about the users and their web surfing habits. Utilising these data, the advertisement companies can get valuable insights about the users and their interests. The gathered information can improve the effectiveness of advertisement campaigns by identifying potential customers of a product/service or by identifying purchase patterns. A successful advertisement campaign depends on the company's ability to fully leverage these data assets. As the artificial intelligence flourish with the machine learning models which were offered as a solution for such a problem depending on dataset availability and computation power but the resulting systems suffer from a loss of transparency and interpretability, especially for end-users.In order to overcome the aforementioned problem of explainability of the models, we propose an explainable and interpretable approach to solve this problem. In the first stage, machine learning model will be used to develop a predictive model that is capable of predicting potential customers who are likely to click the advertisement of a particular product/services. This approach is tested on the public advertising dataset. In the second stage, the predictive model is further utilised by local surrogate model initially using Local Interpretable Model-agnostic Explanations (LIME) to locally approximating the model around a given prediction and then with global interpretable explanations by considering whole machine learning model at once. Finally, Contextual Importance and Utility (CIU) is used for global explanations to generate the explanations and interpretation of the prediction based on the contributing features of the dataset.