With the rapid development of machine learning applications and the re-introduction of variable fonts technology in 2016, UI/UX designers are faced with the challenge of creating responsive user interfaces more often than ever. Contrary to the pre-machine learning era, when variable fonts were mostly found unresponsive in interfaces and lacked widespread adoption, the advent of machine learning has ushered in a new era of typographic refinement, where variable fonts can dynamically learn and adapt to user preferences and behavioral patterns in real-time on the device, leading to a more personalized and engaging user experience. This new synergy requires innovative methodological approaches that go beyond traditional frameworks of classical responsive design. It involves a thorough exploration of personalized font data-driven insights for user behaviors, contextual awareness, adaptive layouts, conversational interactions, and personalized human-computer interaction. Thus, the aim of the research is to investigate how the Intersection of machine learning can be used with variable fonts to influence the overall evolution of user interfaces from a non-intelligent design to a smart, context-aware entity. The developed methodology is based on empirical research which is used continuously throughout the research. The outcomes are evaluated through interviews and peer reviews, then applied, tested and validated in practice to the design of a visual high-fidelity interactive prototype.