This followed with the creation of cryptocurrencies which paved the way for blockchain technologies that has completely transformed the financial landscape with secure, decentralized transfers. However, on the reverse side, such innovation has also brought equally sophisticated fraudulent activities that currently pose a risk to the integrity of these systems. This paper presents a novel approach to the area of enhancement of the security of blockchains and cryptocurrencies by utilizing transfer learning for fraud detection. This platform identifies unusual and potentially fraudulent activities by using pre-trained deep learning models that were fine-tuned on blockchain transaction datasets. Transfer learning allows a model to use knowledge gained in neighboring domains, significantly shortening the training phase and improving detection accuracy, even with a limited amount of labeled data. This is then leveraged in the course of adaptation to fine-tune these models to specific characteristics of the information contained in blockchain transactions, such as different correlation and behavior patterns predicted by fraud. Compared to the traditional methods demanding large volumes of labeled data and lengthy training times, our methodology produces superior results with shorter training times. Transfer learning will aid in speeding up the development of efficient fraud detection algorithms based on the knowledge already acquired, in the absence of enough labeled blockchain-specific data. Experimental results show that the suggested method performs better than conventional machine learning algorithms with respect to fake pattern detection. The model reduces the false positives and negatives by increasing the precision and F1-score. The system further shows resilience and scalability in dynamic situations by adapting to changing fraud patterns. Keywords: random forest, SVM, blockchain, cryptocurrency, security, transfer learning, fraud detection.