This paper provides an analysis of existing Directed Acyclic Graph (DAG) based distributed ledger technology (DLT). It proposes a tailored Model for cryptocurrency platforms and related domains, integrating anomaly detection, reputation scoring, and dynamic consensus mechanisms to optimize transaction management and blockchain validation. Anomaly detection using algorithms like Random Forest classifier trained on transaction data to enhance security by identifying fraudulent activities. Reputation scoring dynamically adjusts node reputations based on transaction validity assessments, fostering trust among network participants. The Anti Proof-of-Stake consensus mechanism variants ensures decentralized and secure block validation. More insightful evaluations are under process aimed towards showing improved efficiency and security compared to traditional blockchain systems, with scalability accommodated through modular design. This work contributes insights and strategies for enhancing efficiency, security, and scalability in decentralized cryptocurrency platforms, advancing the field of distributed ledger technology.