Head and neck cancer is a lethal disease, developing from the mucosal layer of upper aerodigestive tract, which includes paranasal sinuses, nasal cavity, pharynx, oral cavity, and larynx. At present, pathology serves as the gold standard for detecting head and neck tumors involving a painful invasive biopsy procedure. A noninvasive test for this prevalent tumor is still lacking, highlighting the need for alternative reliable diagnostic approaches. In the last few years, the utilization of microarray gene expression profiling, along with machine learning tools, has emerged as a promising method for identifying biomarkers in the diagnosis of head and neck cancer. However, the interpretation of the high-dimensional gene expression data plays a vital role in the accurate diagnosis of diseases. The presence of noise and redundancy poses significant challenges in extracting meaningful information. The present investigation aims to demonstrate the effectiveness of machine learning tools in the domain of precision oncology for head and neck cancer. The proposed method integrates the prominent attributes of filter-based feature selection technique and an autoencoder model. In the initial stage, we employ a gene selection process based on mutual information to select those genes that exhibit significant information pertaining to the cancer progression. The chosen autoencoder leverages its ability for preserving the inherent properties of the latent space while generating the features. By employing the mutual information and autoencoder-based dimensionality reduction technique, the proposed approach achieved an average accuracy of 94.2%, using the leave-one-out cross-validation technique, with only 10 selected features. These features possess the potential to serve as biomarkers for the identification of head and neck cancer.