The automobile industry has witnessed significant advancements over the years, with manufacturers striving to understand and cater to the preferences of various customer segments. Predicting target customer segments is crucial for tailoring marketing strategies, designing products, and optimizing inventory levels. Traditional approaches often rely on surveys and market research, but the emergence of machine learning and regression techniques offers a more data-driven and accurate approach. Traditional methods for customer segmentation in the automobile industry typically rely on market research, surveys, and demographic studies. While these approaches provide valuable insights, they may not always capture the full spectrum of consumer preferences, and they can be time-consuming and resource-intensive. The primary challenge is to develop a predictive model that can accurately identify the customer segments most likely to be interested in specific automobile models or features. This involves analyzing various factors such as demographic data, purchasing history, and market trends to make accurate predictions. As the automobile market becomes increasingly competitive, it's essential for manufacturers to precisely target their products and marketing efforts towards the right customer segments. Accurate predictions can lead to improved sales, reduced marketing costs, and enhanced customer satisfaction by offering products that align with consumer preferences. The project aims to revolutionize customer segmentation by leveraging advanced data analytics and machine learning techniques. By training models on extensive datasets of customer information and purchasing behavior, this research endeavors to develop a system capable of autonomously and accurately predicting target customer segments. The integration of deep learning-based algorithms allows for the identification of key factors influencing customer preferences, enabling manufacturers to make data-driven decisions. This advancement holds great promise for optimizing marketing strategies, product design, and inventory management in the automobile industry, ultimately leading to increased customer satisfaction and profitability.