An essential component of the project feasibility assessment is the conceptual cost estimate. In actuality, it is carried out based on the estimator's prior expertise. However, budgeting and cost control are planned and carried out ineffectively as a result of inaccurate cost estimates. The purpose of this article is to introduce an intelligent model to improve modeling approaches accuracy throughout early phases of a project's development in the construction sector. A support vector machine model, which is computationally effective, is created to calculate the conceptual costs of building projects. To get accurate estimates, the suggested neural network model is trained using a cross-validation method. Through the research of the literature and interviews with experts, the cost estimate's influencing elements are determined. As training instances, the cost information from 40 structures is used. Two potent intelligence methods-Nonlinear Regression (NR) and Evolutionary Fuzzy Neural Interface Model (EFNIM)are offered to illustrate how well the suggested model performs. Based on the readily accessible dataset from the relevant literature in the construction business, their results are contrasted. The computational findings show that the intelligent model that is being provided outperforms the other two potent methods. During the planning and conceptual design phase, the inaccuracy is satisfied for a project's conceptual cost estimate. Case studies demonstrate how SVMs may help planners anticipate the cost of construction in an effective and precise manner.