The growth of e-commerce has led to online reviews playing a crucial role in influencing consumer decisions. However, fake reviews can mislead buyers and damage the reputation of legitimate sellers. This paper employs machine learning and deep learning techniques to detect and filter spam reviews through semantic and sentiment analysis and metadata examination. The outcome is more accurate recommendations, enabling users to make informed purchasing decisions without the interference of fake or irrelevant information.