MT-PDCL is a probabilistic logic programming framework that extends traditional definite clause logic using measure-theoretic and probabilistic methods. Traditional probabilistic logic programming frameworks typically require grounding logic programs into discrete propositional representations, which restricts exact inference to finite domains and discrete probability distributions. MT-PDCL allows logical variables to operate natively over continuous measurable spaces by defining stochastic variables over bounded index domains and equipping the interpretation space with standard Borel $\sigma$-algebras. It is based on Continuous Distribution Semantics and models probabilistic rules as mutually independent causal events. Declarative entailment is formally defined through exact Lebesgue integration over the continuous measure space. MT-PDCL introduces a continuous immediate consequence operator that unifies the integration of continuous prior distributions with the evaluation of exact continuous observations. This approach replaces the combinatorial bottleneck of discrete grounding with exact, algebraic, and structurally differentiable inference. Blogger's Review: MT-PDCL framework provides a new perspective and method for probabilistic logic programming, extending traditional definite clause logic to handle continuous probability distributions and measurable spaces, with great potential and application prospects.