Accurate stock forecasting in the Thai capital market remains challenging due to high volatility and pronounced non -linear dynamics in financial time-series. This study conducts a comparative evaluation of machine learning (ML) models for one-day-ahead prediction of daily closing prices of stocks listed on the Stock Exchange of Thailand (SET). Two widely used technical indicators-Simple Moving Average (SMA) and Exponential Moving Average (EMA)-are employed as input features to capture trend and momentum. Five years of daily data (2019–2023) for three large-cap stocks (PTT. BK, BBL.BK, and CP ALL.BK) are used to assess model robustness under multiple market conditions. We examine three train-test splits (70:30, 80:20, 90: 10) to evaluate stability across data regimes. Experiments show that Linear Regression consistently outperforms competing approaches, achieving the highest coefficient of determination (R2) of 0.9758, and producing one-day-ahead forecasts closely aligned with actual closes for PTT.BK, BBL.BK, and CPALL.BK. The findings indicate that a systematic pipeline grounded in carefully selected technical indicators can provide an effective framework for stock-trend analysis and support investment decision-making in the volatile Thai equity market.