Early detection of cancer is essential for improving patient outcomes, increasing survival rates, and reducing treatment costs. This study explores a hybrid neural network model that combines the strengths of AutoRegressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) to enhance the accuracy of early cancer biomarker detection. ARIMA is utilized to identify linear trends in biomarker levels, while ANN captures complex nonlinear patterns, making the model highly adaptable to real-world biological data. To validate its effectiveness, the model was tested on three cancer-specific datasets—TCGA-BRCA (breast cancer), TCGA-LUAD (lung cancer), and TCGA-COAD (colorectal cancer). The experimental results demonstrated a remarkable accuracy of 94.2%, outperforming traditional diagnostic methods such as logistic regression (88.1%) and support vector machines (SVM) (89.5%). Moreover, to enhance interpretability, the model incorporates Feature-Wise Gradient Boosting (FWGB), allowing it to prioritize the most biologically relevant features. A comparative analysis with existing models highlights the proposed model’s scalability, computational efficiency, and robustness across different types of cancer biomarkers. While challenges such as biological variability and real-world deployment remain, this approach presents a promising step toward integrating AI-driven diagnostic tools into clinical decision-making. By assisting oncologists in early cancer detection, this hybrid ANN-ARIMA model has the potential to reduce diagnostic costs, improve treatment planning, and ultimately save lives.