The rapid expansion of mobile money ecosystems has transformed financial inclusion across emerging economies by enabling secure and accessible digital transactions. However, this transformation has also generated substantial cybersecurity risks, particularly SMS-based fraud schemes targeting financially vulnerable users. Fraudulent SMS messages involving phishing links, impersonation attacks, social engineering tactics, and deceptive financial alerts have become increasingly sophisticated, creating significant challenges for mobile money providers and regulatory institutions. This study proposes an intelligent machine learning framework for detecting mobile money fraud through SMS message analysis using data mining, semantic processing, and predictive classification techniques. The research synthesizes theoretical foundations from fraud analytics, machine learning, and mobile financial security literature to construct a scalable fraud detection architecture. The proposed framework integrates text preprocessing, feature engineering, semantic clustering, probabilistic classification, and deep learning-based pattern recognition to improve fraud detection accuracy and adaptability. The study evaluates the effectiveness of supervised and deep learning approaches in identifying fraudulent SMS behavior while examining operational constraints such as dataset imbalance, linguistic variability, and adversarial manipulation. Findings indicate that hybrid machine learning architectures combining semantic analysis with neural network-based classifiers provide superior performance in identifying fraudulent communication patterns. The research contributes a structured analytical model for intelligent fraud detection in mobile money systems and highlights the importance of adaptive machine learning in strengthening digital financial security infrastructures. The paper further discusses implementation limitations, practical implications, and future research directions for intelligent fraud prevention systems in developing economies.