Forecasting time series data with machine learning models is a critical practice in various industries and sectors. Time series data, characterized by its sequential nature and correlation between data points, presents unique challenges and opportunities for predictive modeling. In this article, we will delve into the fundamentals of time series forecasting, exploring techniques for data preprocessing, model selection, training, and evaluation. Additionally, we will discuss advanced methodologies and best practices for effectively implementing machine learning models to forecast time series data accurately and efficiently.