Accurate precipitation intensity forecasting is critical for effective water resource management and flood risk mitigation, particularly in increasing extreme weather events driven by climate change. Traditional machine learning (ML) methods show promise but need evaluation to see if they can outperform complex deep learning (DL) algorithms for accurate, real-time flood forecasting. This study aims to compare the performance of an Improved Extreme Learning Machine (IELM) with two widely used deep learning techniques—Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks—for multi-hour-ahead precipitation intensity forecasting. The goal is to assess if a well-optimized traditional ML model can match or outperform others while being computationally efficient. The IELM was optimized by testing fourteen activation functions, systematically varying the number of hidden neurons (NHN), varying hidden neurons, and comparing forecasts across 1- to 6-hour horizons and four precipitation categories. IELM consistently outperformed CNN and LSTM across all horizons and precipitation categories. For instance, in the critical Very Heavy precipitation category, IELM provided highly accurate predictions while maintaining computational simplicity. Conversely, CNN and LSTM models achieved competitive accuracy but demonstrated higher complexity, limiting their suitability for real-time applications. The findings highlight the capability of optimized traditional ML methods like IELM to effectively address the challenges of precipitation intensity forecasting without relying on complex DL architectures. IELM’s superior performance across all categories and forecasting horizons positions it as a reliable, efficient, and practical tool for real-time applications, particularly in scenarios with high stakes as flash flood forecasting. This study underscores the importance of proper model optimization and offers valuable insights for advancing precipitation forecasting methodologies.