In recent years, interior design has played an increasingly important role in improving the look and usability of residential and commercial spaces. Although professional designers are often employed for this purpose, the process can be time-consuming and costly, with limited flexibility for personalized input. To address these limitations, an AI-assisted solution has been developed. This system employs Conditional Generative Adversarial Networks to analyze photographs of indoor environments alongside text descriptions that reflect user preferences such as desired furniture style, color schemes, and spatial arrangements. After processing the information, the tool provides a range of design suggestions tailored to the user’s specific needs. This method eliminates the need for repeated consultations and allows for rapid generation of unique, realistic interior layouts. The approach supports a more inclusive and affordable design experience, enabling individuals to explore personalized decor ideas efficiently. By merging visual data with linguistic inputs, the system presents a novel pathway for intuitive and responsive interior design support.