The　imbalance　between　parking　availability　and　demand　has　led　to　a　rise　in　traffic　challenges　in　many　cities.　The　adoption　of　technologies　like　the　Internet　of　Things　and　deep　learning　algorithms　has　been　extensively　explored　to　build　automated　smart　parking　systems　in　urban　environments.　Non-human-mediated,　scalable　smart　parking　systems　that　are　built　on　decentralized　blockchain　systems　will　further　enhance　transparency　and　trust　in　this　domain.　The　presented　work,　PARKTag,　is　an　integration　of　a　blockchain-based　system　and　computer　vision　models　to　detect　on-field　free　parking　slots,　efficiently　navigate　vehicles　to　those　slots,　and　automate　the　computation　of　parking　fees.　This　innovative　approach　aims　to　enhance　the　efficiency,　scalability,　and　convenience　of　parking　management　by　leveraging　and　integrating　advanced　technologies　for　real-time　slot　detection,　navigation,　and　secure,　transparent　fee　calculation　with　blockchain　smart　contracts.　PARKTag　was　evaluated　through　implementation　and　emulation　in　selected　areas　of　the　MIT　Art　Design　Technology　University　campus,　with　a　customized　built-in　dataset　of　over　2000　images　collected　on-field　in　different　conditions.　The　fine-tuned　parking　slot　detection　model　leverages　pre-trained　algorithms　and　achieves　significant　performance　metrics　with　a　validation　accuracy　of　92.9%　in　free　slot　detection.　With　the　Solidity　smart　contract　deployed　on　the　Ethereum　test　network,　PARKTag　achieved　a　significant　throughput　of　10　user　requests　per　second　in　peak　traffic　hours.　PARKTag　is　implemented　as　a　mobile　application　and　deployed　in　the　mobile　application　store.　Its　beta　version　has　undergone　user　validation　for　feedback　and　acceptance,　marking　a　significant　step　toward　the　development　of　the　final　product.　©　2024　by　the　authors.