In the computer world, ransomware continues to evolve as a significant threat to cybersecurity, employing increasingly sophisticated techniques to evade detection and cause widespread disruption across industries. The novel approach presented in this research leverages opcode sequences combined with machine learning to address the limitations of traditional static analysis methods, offering a more robust and accurate detection mechanism. Through the use of t-SNE for dimensionality reduction and SVM for classification, this method efficiently handles high-dimensional opcode data, allowing for precise identification of ransomware samples. The experimental results show substantial improvements in detection accuracy, precision, and F1-score compared to baseline models, while demonstrating the importance of feature reduction in optimizing performance. Despite the computational overhead introduced through t-SNE, the overall findings indicate that the proposed framework enhances the static detection of ransomware, making it a valuable tool for cybersecurity efforts aimed at mitigating evolving threats.