In today’s digital age, consumers encounter countless choices when selecting products or services, leading to aphenomenon known as choice overload. The Dynamic Choice Advisor aims to alleviate this issue by leveraging machine learningalgorithms to analyse user preferences and generate personalized recommendations. This paper explores the Advisor's design andfunctionality, detailing how it enhances the decision-making process by offering users customized options. The Dynamic ChoiceAdvisor achieves this by collecting and processing user data, allowing it to understand individual preferences over time and adaptrecommendations accordingly. The system is tested in real- world applications, demonstrating improved user satisfaction anddecision efficiency. Unlike traditional recommendation models, which rely on static datasets, the system dynamically adjusts toshifting user preferences in real-time, ensuring accurate and relevant suggestions. Additionally, this paper examines the system’srequirements, working process, and findings, providing insights into the benefits and potential limitations of a personalizedrecommendation system. The Dynamic Choice Advisor contributes to the ongoing research in recommendation systems, offering apractical solution for reducing decision fatigue and enhancing the user experience.Keywords: Personalized Recommendation, Dynamic Choice Advisor, Machine Learning, User Preferences, Real-time DataProcessing, Decision-Making, Recommendation Systems, Collaborative Filtering, Content-Based Filtering.