Using linear regression for house price prediction involves analyzing data on various house features like size, location, and number of bedrooms to build a model that estimates house prices based on these factors. The model learns the relationship between these features and prices through training on historical data, enabling it to make predictions for new houses. This method is valuable for real estate analysis and pricing due to its simplicity and interpretability.