Price is a critical determinant in financial activities, and sudden fluctuations in price often signal market instability. Machine learning offers robust techniques to forecast product prices and address these instabilities effectively. This study examines the application of machine learning models to predict onion prices in India, utilizing data collected from the Ministry of Agriculture, India. Various machine learning algorithms, including K-Nearest Neighbor (KNN), Naïve Bayes, Decision Tree, Neural Network (NN), and Support Vector Machine (SVM), were employed for classification purposes. Their performance was evaluated to identify the most accurate model. Additionally, this research integrates deep learning approaches, specifically Long Short-Term Memory (LSTM) networks, for forecasting onion prices. These methods classify onion prices into three categories: preferable (low), economical (mid), and expensive (high), providing valuable insights to address market volatility.