The financial cost estimation of metal products in the machining sector is a significantly important aspect in industrial and equipment precision facilities. There are multiple methods for machining cost prediction before the products are manufactured, although each method has its own costs and varying levels of complexity and needs. However, the necessity for an economic approach to calculate the financial cost of machining a particular product has rapidly grown in recent years; this is particularly true when the product is manufactured in small numbers and limited financial resources. Current traditional approaches to calculate the cost of production in small industrial facilities proved complex and inaccurate due to the unlimited variety of workpiecesWorkpiece in terms of features and dimensions. Calculating the manufacturingManufacturing cost of a workpieceWorkpiece as a function of machining timeMachining time is a standard approach, but determining the entire manufacturingManufacturing time is an expensive and time-consuming procedure in and of itself. Machine learningMachine learning techniques show a significant implication for predicting the production cost and time in different engineering sectors, which reduce the time consuming for estimating procedure that has a direct effect on the product price and the profit of enterprises. Consequently, our study contributes to developing a parametric costParametric cost estimating technique using artificial intelligence (AI) to predict total production cost and/or time more efficiently. The new approach requires a great deal of manufacturingManufacturing knowledge, where the skilled human factor comes in which basically requires experts that provide accurate cost estimation of producing parts, particularly complex shapes. Our approach takes into consideration the need for a substantial amount of learned knowledge that is susceptible to change as the expert accumulates more knowledge and expertise in the system. The study covers small medium enterprises (SME) in different job shop facilities that mainly deal with the automotive sector and produce spare parts with small batch size in Oman. The data collected from different experts in Muscat provides an accurate parametric estimate of cost and time production of spare parts. The result of our study is likely to produce an advanced estimator based on an artificial neural network (ANN) that is designed to replace traditional approaches for estimating total manufacturing time and costs, which depended on either an expert engineer or a thorough operation sheet. The new model is expected to predict overall production time using training instances derived from real-world machining processes.