<p>This research investigates the impact of scaling on the fractal dimension of product iterated function systems and the underlying structure of self-similar sets. By employing a comprehensive analysis framework, we explore how scaling affects the intricate properties of self-replicating patterns generated by iterated function systems. Specifically, our study focuses on the estimation of fractal dimensions for attractors formed through the combination of scaled iterated function systems, shedding light on the scaling behavior within self-similar sets. Additionally, we delve into the structural characteristics of scaled iterated function systems, elucidating their role in shaping the overall organization of self-replicating structures.</p>