The research examines the potential of marine sediment waste to develop sustainable and budget-friendly concrete materials. The study employs Random Forest and Support Vector Regression (SVR) machine learning algorithms to identify the optimal sediment addition amount and resulting concrete strength. The research analyzes 104 samples based on four essential factors which consist of particle size distribution and cleanliness and fineness modulus and mechanical performance. The Random Forest model outperformed SVR because it achieved R2 values above 0.98 and RMSE results under 0.20 MPa which shows its ability to identify complex data patterns. The research found that concrete strength and construction material suitability depend on the geographic origin of sediment materials. Artificial intelligence is becoming more common in environmental engineering to make more use of marine sediment residues. Instead of relying only on empirical testing techniques, the researchers used AI models to predict the compressive strength of the mixture, then subsequently validated these predictions with actual results generated through laboratory testing. The importance of comparing predictions and measures is twofold, both are important. First, the researcher was able to determine how much sediment could be added to the mixture before congesting the mixtures’ its compressive strength.